Past Seminars
January 9, 2024: (Special Time: 10AM, Physics Fellow Candidate) – Dominic Skinner, Northwestern University
Statistical Physics Of Embryonic Transcriptomes Reveals Map Of Cellular Interactions.
Host: Eric Siggia
Starting from one totipotent cell, complex organisms form through a series of differentiation events, resulting in a multitude of cell types. In the ascidian embryo, differentiation happens early. For instance, by the 32 cell stage there are at least 10 transcriptomically distinct cell states. Moreover, cells coordinate within the embryo to differentiate in an extremely precise spatial pattern. Using recent single-cell sequencing data of early ascidian embryos, we leverage natural variation together with techniques from statistical physics to investigate development at the level of a complete interconnected embryo. After robustly identifying distinct transcriptomic states or cell types, a statistical analysis reveals correlations within embryos and across cell types beyond mean expression levels. From these intra-embryo correlations, we infer minimal networks of cell-cell interactions using regularization and spin glass-like models of interacting systems, revealing spatial connections that are of key importance in development.
January 10, 2024: (Special Time: 12:00PM) – Uri Alon, Weizmann Institute of Science
Mathematical Essence Of Aging.
Host: Stan Leibler
Aging shows nearly universal quantitative patterns. We explain them using a stochastic ODE for damage production and removal, deduced from experiments on damage dynamics in mice and in individual bacteria, the latter done by us. This simple model explains a wide range of phenomena in human aging and age-related diseases, as well as in model organisms. It pinpoints core molecular and cellular drivers of aging, and suggests interventions that, at least in mice, can compress the relative sick span (fraction of lifespan that an individual is disabled).
January 11, 2024: (Special Time: 10AM, Physics Fellow Candidate) – Daniel Barabasi, Harvard University
Nature Over Nurture: How Complex Computations Emerge From Developmental Priors.
Host: Eric Siggia
An important challenge of modern neuroscience is to unveil the mechanisms shaping the wiring of the connectome, answering the difficult question of how the brain wires itself. Neuronal systems display a high degree of wiring reproducibility, such that multiple circuits and architectural features appear to be identical within a species, invariants that network models are unable to explain. This is because the architectures of neural circuits aren’t fully learned, as recent advances in systems neuroscience and AI would have us believe, nor is our wiring directly determined by our DNA; instead, our genome provides assembly rules that cells use to self-organize into a functional brain, much like how cellular automata’s simple patterns generate complex emergent behavior. To illustrate how complex computations can emerge from seemingly noisy, self-assembling processes, I derive a neurodevelopmental encoding of artificial neural networks that considers the weight matrix of a neural network to be emergent from well-studied rules of neuronal compatibility. Rather than updating the network’s weights directly, we improve task fitness by updating the neurons’ wiring rules, thereby mirroring evolutionary selection on brain development. We find that our model (1) provides sufficient representational power for high accuracy on ML benchmarks while also compressing parameter count, and (2) can act as a regularizer, selecting simple circuits that provide stable and adaptive performance on metalearning tasks. Finally, I will discuss how such physical models of directed, self-assembling systems can both (1) advance developmental understanding and (2) provide a fresh perspective on how evolution balances nature and nurture in neural circuits.
January 16, 2024: (Special Time: 10AM, Physics Fellow Candidate) – Francois Bourassa, McGill University
Theory Of Antigen Encoding And Cross-Receptor Interactions In T Cell Immunotherapy.
Host: Eric Siggia
The mechanisms connecting early T cell receptor (TCR) activation to complex T cell responses in the immune system have not been fully elucidated. Understanding these processes quantitatively is however crucial to fine-tune immunotherapy treatments against cancer. To systematically map out T cell activation, the lab of Grégoire Altan-Bonnet has developed a robotic platform which tracks over days the dynamics of messenger proteins, called cytokines, produced by T cells to communicate with other cells. We found a low-dimensional representation of high-dimensional cytokine dynamics in which trajectories are ordered according to antigen strength. We termed this property “antigen encoding” and quantified it using information theory and nonlinear dynamical equations. We then leveraged these insights to disentangle cross-receptor interactions in chimeric antigen receptor (CAR) T cells used in cancer immunotherapy. In particular, we developed an adaptive model of receptor proofreading to explain antagonism (i.e. inhibition) of CAR activation by weak TCR stimulation. Our model predictions quantitatively matched experimental data, enabling us to engineer antagonism to reduce CAR T cell toxicity against healthy tissues.
January 18, 2024: (Special Time: 10AM, Physics Fellow Candidate) – Jialong Jiang, California Institute of Technology
Revealing Regulatory Network Organization Through Single-Cell Perturbation Profiling And Maximum Entropy Models.
Host: Eric Siggia
Gene regulatory networks control cellular information processing and response to signals and environmental changes. Perturbations are widely used in genetics to decode gene interactions, yet how to extract regulatory network models from large-scale single-cell perturbation profiling remains a significant challenge. We develop the framework, D-SPIN, that constructs regulatory network models from single-cell data collected across thousands of perturbations. Using the maximum entropy principle, D-SPIN identifies a unified regulatory network model of the single-cell population and the effects of each perturbation on the gene program level. D-SPIN enables accurate network reconstruction and provides a reasoning framework of how cell states are constructed through pairwise interactions between gene programs. Using genome-wide Perturb-seq data, D-SPIN reveals different strategies of homeostasis regulation in cancer cell stress response. Using drug profiling data, D-SPIN dissects response in heterogeneous cell populations to elucidate how drug combinations induce novel cell states through additive recruitment of gene programs. Moreover, D-SPIN facilitates perturbation design by finding sloppy model parameters and informative perturbations to pindown these parameters. The framework extends to a wide range of applications including signaling responses of immune populations and identifying cell-cell interaction networks from spatial transcriptomics profiling. In conclusion, D-SPIN provides a computational framework for constructing interpretable models of gene regulatory networks to reveal principles of cellular information processing and physiological control, and strategies for designing perturbation for efficient network inference.
February 15, 2024: (Special Time: 10AM, Physics Fellow Candidate) – Nikolas Schonsheck, University of Delaware
Detecting And Learning Cyclic Structures In Neural Population Coding.
Host: Eric Siggia
Cyclic structures are a class of mesoscale features ubiquitous in both experimental stimuli and the activity of neural populations encoding them. Important examples include encoding of head direction, grid cells in spatial navigation, and orientation tuning in visual cortex. While cyclic structures are difficult to detect and analyze with classical methods, tools from the mathematical field of algebraic topology have proven to be particularly effective in understanding cyclic structures. Recently, work of Yoon et al. develops a topological framework to match cyclic coding patterns in distinct populations that encode the same information. We leverage this framework to study the efficacy of Hebbian learning rules in propagating cyclic structures through neural systems. Our primary results are 1) feedforward networks with connections drawn from inhibitory-biased random distributions do not reliably propagate cyclic features of neural coding 2) updating network connections with a biologically realistic Hebbian learning rule modeling spike timing dependent plasticity robustly constructs networks that do propagate cyclic features and 3) under biologically plausible parameter choices, the inhibition and propagation of such features can be modulated by the size of the output neuron population.
February 27, 2024: (4PM) – Noam Shental, Open University of Israel
High-Resolution Microbial Profiling Of Novel Niches And A Pan-Microbiome Knowledge Base.
Host: Orli Snir
I will present two computational tools for microbiome research recently developed by my group – the first allows high-resolution microbial profiling of novel niches (https://www.biorxiv.org/content/10.1101/2023.09.03.556087v1, under review), and the second is a bacterial knowledge base that has collected more than 1.5 million sequence-to-phenotype associations and allows extracting pan-microbiome biological insights (https://academic.oup.com/nar/article/51/13/6593/7199329).
March 5, 2024: (4PM) – Liat Shenhav, New York University
It’s About Time: Ecological And Eco-Evolutionary Dynamics Across The Scales.
Host: Bertrand Ottino-Loffler
Complex microbial communities play a vital role across many domains of life, from the female reproductive tract, through the oceans, to the plant rhizosphere. The study of these communities offers great opportunities for biological discovery, due to the ease of their measurement, the ability to perturb them, and their rapidly evolving nature. Yet, their complex composition, dynamic nature, and intricate interactions with multiple other systems, make it difficult to extract robust and reproducible patterns from these ecosystems. To uncover their latent properties, I develop models that combine longitudinal data analysis and statistical learning, and which draw from principles of community ecology, complexity theory and evolution. I will briefly present methods for decomposition of microbial dynamics at an ecological scale (Shenhav et al., Nature Methods; Martino & Shenhav et al., Nature Biotechnology). Using these methods we found significant differences in the trajectories of the infant microbiome in the first years of life as a function of early life exposures, namely mode of delivery and breastfeeding. I will then show how incorporating eco-evolutionary considerations allowed us to detect signals of purifying selection across ecosystems. I will demonstrate how interactions between evolution and ecology played a vital role in shaping microbial communities and the standard genetics code (Shenhav & Zeevi, Science, Liao & Shenhav Nature Comm.). Inspired by these discoveries, I am expanding the scope beyond the microbiome, modeling multi-layered data on human milk composition. I will present results from an ongoing study in which I am building integrative models of nasal, gut and milk microbiota, combined with human milk components, to predict infant respiratory health. I found that the temporal dynamics of microbiota in the first year of life, mediated by milk composition, predict the development of chronic respiratory disease later in childhood. These models, designed to identify robust spatiotemporal patterns, would help us better understand the nature and impact of complex ecosystems like the microbiome and human milk from the time of formation and throughout life.
March 19, 2024: (4PM) – Mason Porter, University of California, Los Angeles
Topological Data Analysis Of Spatial Systems.
Host: Bertrand Ottino-Loffler
I will discuss topological data analysis (TDA), which uses ideas from topology to quantify the “shape” of data. I will focus in particular on persistent homology (PH), which one can use to find “holes” of different dimensions in data sets. I will start by introducing these ideas and will discuss a series of examples of TDA of spatial systems. The examples that I’ll discuss include voting data, the locations of polling sites, the spread of COVID-19, and the webs of spiders under the influence of various drugs.
March 26, 2024: (4PM) – Stefano Di Talia, Duke University
Encoding Tissue Size And Shape During Vertebrate Regeneration.
Host: Woonyung Hur
Some animals have a remarkable ability to regenerate appendages and other damaged organs. I will focus on our attempts to reveal novel quantitative principles for the control of regeneration in zebrafish. I will describe how signaling waves and long-range gradients are used to control tissue growth and facilitate that tissues grow back to their correct size and shape.
April 2, 2024: (4PM) – Terry Hwa, University of California, San Diego
Quantitative Rules Govern Protein Expression And Activity Across The Bacterial Phylogeny.
Host: Eric Siggia
Distinct bacterial species thrive under distinct growth conditions. Even species sharing similar optimal conditions can grow at vastly different rates; e.g., Vibrio natriegens grows more than 50% faster than E. coli and B. subtilis in the same common growth media at 37C. What do the super-fast growers do differently? Quantitative proteomics reveal surprisingly rigid programs of proteome allocation for bacteria irrespective of the phylogeny, distinguished mainly by the speed of their enzymes and by their metabolic orientations.
April 16, 2024: (4PM) – Michail Tsodyks, Institute for Advanced Studies
Studying Human Memory for Random and Meaningful Material: A Comparative Study.
Host: Merav Stern
We consider the recognition and recall experiments on random lists of words vs meaningful narratives. A mathematical model based on a specific recall algorithm of random lists established the universal relation between the number of words that is retained in memory and the number of words that can on average be recalled, characterized by a square root scaling. This relation is expressed by an analytical expression with no free parameters and was confirmed experimentally to a surprising precision in online experiments. In order to extend this research to meaningful narratives, we took advantage of recently developed large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary. We developed a pipeline for designing large scale memory experiments and analyzing the obtained results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different lengths. We found that both recall and recognition performance scale linearly with narrative length. Furthermore, in order to investigate the role of narrative comprehension in memory, we repeated these experiments using scrambled versions of the presented stories. We found that even though recall performance declined significantly, recognition remained largely unaffected. Interestingly, recalls in this condition seem to follow the original narrative order rather than the scrambled presentation, pointing to a contextual reconstruction of the story in memory.
April 23, 2024: (4PM) – Danny Abrams, Northwestern University
Careful Or Colorful? The Evolution Of Animal Ornaments.
Host: Bertrand Ottino-Loffler
Extravagant and costly ornaments (e.g., deer antlers or peacock feathers) are found throughout the animal kingdom. Charles Darwin was the first to suggest that female courtship preferences drive ornament development through sexual selection. In this talk I will describe a minimal mathematical model for the evolution of animal ornaments and will show that even a greatly simplified model makes nontrivial predictions for the types of ornaments we expect to find in nature.
April 30, 2024: (4PM) – Vikram Gadagkar, Columbia University
Neural Mechanisms Of Performance Evaluation In Singing Birds.
Host: Philip Kidd
Many behaviors are learned through trial and error by matching performance to internal goals, yet neural mechanisms of performance evaluation remain poorly understood. We recorded basal ganglia–projecting dopamine neurons in singing zebra finches as we controlled perceived song quality with distorted auditory feedback. Dopamine activity was suppressed after distorted syllables, consistent with worse-than-predicted performance, and activated when a predicted distortion did not occur, consistent with better-than-predicted performance. Thus, dopaminergic error signals can evaluate behaviors that are learned, not for reward, but by matching performance to internal goals. We then developed new computational methods to show that spontaneous dopamine activity correlated with natural song variations, demonstrating that dopamine can evaluate natural behavior unperturbed by experimental events such as cues, distortions, or rewards. Attending to mistakes during practicing alone provides opportunities for learning, but self-evaluation during audience-directed performance could distract from ongoing execution. It remains unknown how animals switch between, and process errors during, practice and performance modes. When male zebra finches transitioned from singing alone to singing female-directed courtship song, singing-related error signals were reduced or gated off and dopamine neurons were instead activated by female calls. Dopamine neurons can thus dynamically re-tune from self-evaluation to social feedback during courtship.
May 9, 2024: (4PM) – Arjun Karuvally, University of Massachusetts-Amherst
Hidden Traveling Waves in Artificial Recurrent Neural Networks Encode Working Memory.
Host: Marcelo Magnasco
Traveling waves are integral to brain function and are hypothesized to be crucial for short-term information storage. This study introduces a theoretical model based on traveling wave dynamics within a lattice structure to simulate neural working memory. We theoretically analyze the model’s capacity to represent state and temporal information, which is vital for encoding the recent history in history-dependent dynamical systems. In addition to enabling robust short-term memory storage, our analysis reveals that these dynamics can alleviate the diminishing gradient problem, which poses a significant challenge in the practical training of recurrent neural architectures. We explore the model’s application under two boundary conditions: linear and non-linear, the latter driven by self-attention mechanisms. Experimental findings show that randomly initialized and backpropagation-trained Recurrent Neural Networks (RNNs) naturally exhibit linear traveling wave dynamics, suggesting a potential working memory mechanism within these networks. This mechanism remains concealed within the high-dimensional state space of the RNN and becomes apparent through a specific basis transformation proposed by our model. In contrast, the non-linear scenario aligns with autoregressive loops in attention-based transformers, which drive the AI revolution. The results highlight the profound impact of traveling waves on artificial intelligence, improving our understanding of existing black-box neural computation and offering a foundational theory for future enhancements in neural network design.
May 14, 2024: (4PM) – Jorn Dunkel, Massachusetts Institute of Technology
Quantitative Model Inference For Living Matter.
Host: Eric Siggia
Recent advances in live-imaging techniques provide dynamical data ranging from the cellular to the organism scale. Notwithstanding such experimental progress, quantitative theoretical models often remain lacking, even for moderately complex classes of biological systems. Here, I will summarize our ongoing efforts to implement computational frameworks for inferring predictive dynamical equations from multi-scale imaging data. As specific examples, we will consider models for cell locomotion, neural dynamics, mosquito flight behavior, and collective animal swarming.
January 3, 2023: (Special Time: 10AM via Zoom Only, Physics Fellow Candidate) – Xiaowen Chen, Ecole Normale Superieure
Inferring Collective Dynamics In Groups of Social Mice.
Host: Bertrand Ottino-Loffler
Social interactions are a crucial aspect of behavior in human society and many animal species. Nonetheless, it is often difficult to distinguish the effect of interactions from independent animal behavior (e.g. non-Markovian dynamics, response to environmental cues, etc.). I will address this question in social mice, where we infer statistical physics models for the collective dynamics for groups of mice, housed and location-tracked over multiple days in a controlled yet ecologically-relevant environment. We reproduce the distribution for the co-localization patterns using pairwise maximum entropy models. The inferred interaction strength is biologically meaningful and can be used to characterize sociability for different mice strains. Moreover, these models can distinguish the effect of change of prefrontal cortex plasticity due to social-impairment drugs, and useful to study autism in the mice model. The equilibrium dynamics on the resulting model can successfully predict the transition rates, but not the waiting time distribution. Inspired by the observed long-tailed waiting time distributions in the mice, we have developed a novel inference method that can tune the dynamics while keeping the steady state distribution fixed. Constructed through a non-Markovian fluctuation-dissipation theorem, this new inference method, termed the “generalized Glauber dynamics”, addresses an important question in statistical inference, for which I will derive the expression, demonstrate its power, and show how to infer the model using examples of Ising and Potts spins. Finally, we will apply the generalized Glauber dynamics to the social mice data and show how memory is important in collective animal behavior.
January 5, 2023: (Special Time: 2PM via Zoom Only, Physics Fellow Candidate) – Merav Stern, University of Oregon and The Hebrew University of Jerusalem
Modeling Neural Networks Reveals How Neural Circuitry Transforms Space Into Time.
Host: Liat Shenhav
The connectivity structure of many biological systems, including neural circuits, is highly non-uniform. Recent developments in optogenetic tools allow mapping in detail these irregularities in neural connectivity. But our understanding of these maps, including the contribution of each connectivity component to the overall circuitry dynamics, is still lacking. I will present analytical tools from complex system studies which enable us to isolate the impact of each connectivity component by astute reduction of the network description. I will bring a few specific examples of non-uniform connectivity, including cell-type dependent connectivity and clustering. I will show how they enrich the network dynamics and transform spatial properties into timing mechanisms.
January 12, 2023: (Special Time: 10AM, Physics Fellow Candidate) – Avaneesh Narla, University of California, San Diego
Dynamic Coexistence Due To Growth Succession In Cyclic Microbial Ecosystems.
Host: Eric Siggia
Microbial ecosystems are commonly modeled by fixed interactions between microbes in steady physiological states, typically the exponential growth state. However, ecological dynamics often feature large self-generated environmental changes which drive microbes through distinct physiological states manifested by very different growth rates. Examples of such dynamics include succession dynamics in nature and simple growth-dilution cycles in the laboratory. Here, we introduce a phenomenological model to gain insight into the dynamic coexistence of microbes due to changes in physiological states in cyclic environments. Our model allows us to bypass specific interactions leading to different physiological states (e.g., nutrient starvation, stress, aggregation, contact-dependent killing, etc.), by considering the growth of each species according to a global ecological coordinate, taken here to be the total community biomass. Analysis of this model provides rigorous, quantitative criteria for the dynamic coexistence of many species in terms of differential species’ dominance (“growth niche”) along the ecological coordinates. Our model shifts the focus of ecosystem dynamics from bottom-up studies based on inter-species interaction to top-down studies based on accessible macroscopic observables such as growth rates and total biomass, thereby allowing quantitative examination of community-wide characteristics.
January 19, 2023: (Special Time: 10AM, Physics Fellow Candidate) – Caroline Holmes, Princeton University
Sensing And Encoding Problems, From Circadian Clocks to Photoreceptors.
Host: Ben Weiner
I will discuss two problems that I have worked on in my PhD, both of which have involved considering how biological systems encode information about their environments. In the first of these, I considered circadian clocks as an encoding problem. We found that the constraint of decodability significantly restricts the parameter space of circadian clocks, and that it may explain the long-standing puzzle of non-24 hour internal periods of circadian clocks. In the second section, I will discuss ongoing work on photoreceptor arrangement. This is an interesting problem in itself, as many organisms have qualitatively different photoreceptor arrangements, but it also has been a lens into thinking more deeply and generally about the relationship between optimality and variability.
February 21, 2023: (4PM) – Lisa Stein, University of Alberta
Applying Systems Biology to Resolve Microbial Metabolism of Greenhouse Gases.
Host: Ben Weiner
Technologies to reduce GHG emissions must take microorganisms into account as this invisible majority is largely responsible for production and consumption of methane and nitrous oxide, the second and third most important GHGs in causing global warming. Methanogenesis produces most of the methane emitted to the atmosphere, whereas methanotrophic microbes account for methane consumption prior to its emission, plus ca. 1% of atmospheric methane consumption. The primary source of nitrous oxide to the atmosphere is from nitrifying and denitrifying microorganisms whose activity has accelerated over the past 60 years due to anthropogenic input of reactive-N to the biosphere. Interestingly, methanotrophic and nitrifying microbes share common enzymes and metabolic pathways, enabling both groups to produce or consume GHGs depending mainly on redox potential and nitrogen availability. Using the bacterium Methylomicrobium denitrificans FJG1 as a model system, we collected RNAseq, proteomics and metabolomics data across 6-point growth curves to examine the dynamics of methane consumption and nitrous oxide production as a function of oxygen and nitrogen availability. A gene regulatory network of the RNAseq data showed different topologies with either ammonium or nitrate as the N-source, as nitrate is required to induce methane-dependent denitrification to nitrous oxide. With a genome-scale metabolic model under construction, the omics data point to a division of labor in M. denitrificans between fermentation and denitrification at the onset of anoxia. Extrapolating to natural ecosystems, a similar division of labor has been observed in anoxic freshwater lakes wherein methanotrophs use alternative electron acceptors to consume methane while providing organic molecules from fermentation to cross-feed other microbial populations. However, when nitrate is the alternative electron acceptor, nitrous oxide is produced proportionally to methane consumption. This case-study demonstrates the need to consider interconnectedness and coevolution of microbial functionality, and to apply omics-based systems biology models when developing and implementing GHG reduction strategies at ecosystem scale.
February 28, 2023: (4PM) – Arup Chakraborty, Massachusetts Institute of Technology
The Antibody Response to Mutable Viruses.
Host: Bertrand Ottino-Loffler
Infectious disease-causing pathogens have plagued humanity since antiquity, and the COVID-19 pandemic has been a vivid reminder of this perpetual existential threat. Vaccination has saved more lives than any other medical procedure, and indeed, effective vaccines have helped control the COVID-19 pandemic. However, we do not have effective vaccines against rapidly mutating viruses, such as HIV; nor do we have a universal vaccine against seasonal variants of influenza or SARS-CoV-2 variants that continue to evolve. The ability to develop effective universal vaccines that protect us from variant strains of mutable viruses will help create a more pandemic-resilient world. In this talk, I will describe how by bringing together approaches from statistical physics, virology and immunology, progress is being made to address this challenge. In particular, I will focus on approaches that aim to design vaccines and immunization strategies that elicit antibodies that can protect against diverse mutant strains. As I hope to show, this is a problem at the intersection of statistical physics, evolutionary biology, immunology, and vaccine development. The application of fundamental concepts to HIV, influenza and SARS-CoV-2 vaccines will be discussed.
March 14, 2023: (4PM) – Jennifer A.N. Brophy, Stanford University
Reprogramming Plant Development Using Synthetic Genetic Circuits.
Host: Ben Weiner
Structural features of a plant contribute to its ability to survive in challenging environments. For example, the size and shape of a plant’s root system influences its ability to reach essential nutrients in the soil or to acquire water during drought. Yet, our understanding of relationships between form and function remain limited. We are using synthetic gene circuits to modify the size and shape of plants so that we can test the contribution of specific plant features to environmental stress tolerance. A better understanding of the plant features that are important for environmental stress tolerance would enable targeted breeding and biotechnological interventions that strengthen our agricultural systems.
March 16, 2023: (4PM) – David Zeevi, Weizmann Institute of Science
Investigating Human Effects on Natural Microbial Communities.
Host: Liat Shenhav
Microbial ecosystems are critical for supporting life on Earth, regulating global nutrient cycles, greenhouse gas exchange, and disease transmission and protection. Yet, there is increasing evidence that natural microbial communities are affected by anthropogenic stress from pollutants, fertilizers and land-use changes, among other factors. In my lab, we study human effects on microbial communities. Our ultimate goal is to discover microbe-based modalities for upcoming challenges, such as food security in an increasingly warm and polluted planet. Common approaches for studying the response of microbial communities to human intervention usually depend on comparisons between environments (e.g., natural and managed). However, microbial communities are affected by many factors, making any two such environments different and increasing the risk of spurious findings. In addition, most species and genes in many of these environments are uncharacterized, greatly limiting the discovery of microbe-based modalities. In my talk, I will discuss the approaches we undertake to advance discovery while addressing these issues, including the development of novel computational methods and the collection of unique data.
April 5, 2023: (Note: This is a Wednesday seminar – 4PM) – Jan Skotheim, Stanford University
How Cells Coordinate Growth and Division.
Host: Eric Siggia
Cell size is fundamental to function in different cell types across the human body because it sets the scale of organelle structures, surface transport, and, most importantly, biosynthesis. While some genes affecting cell size have been identified, the molecular mechanisms through which cell growth drives cell division had remained elusive. While it was expected that growth would act to increase the activities of the cyclin-dependent kinases (Cdk) known to promote cell division, this is not the case. Rather, we found that cell growth acts in the opposite manner. Cell growth triggers division by diluting proteins that inhibit cell division, Whi5 in yeast, and the retinoblastoma tumor suppressor Rb in human cells. Thus, inhibitor dilution provides one long sought mechanism coupling cell growth to cell division and it relies on the differential scaling of the biosynthesis of cell cycle activators and inhibitors molecules. How are some molecules synthesized to remain in proportion to cell size while others are synthesized in amounts that are independent of cell size? We have begun to elucidate the molecular mechanisms underlying size scaling across the proteome and have uncovered both transcriptional and post-transcriptional mechanisms that tune protein concentrations to enhance cellular function and control cell size.
April 25, 2023: (4PM) – Daniel H. Buckley, Cornell University
The Secret Life of Soil Microbes: Unearthing the Basis of the Terrestrial Carbon Cycle.
Host: Ben Weiner
Soils comprise one of the largest pools of active carbon on the planet. The carbon storage potential of soil is governed by the dynamic balance of microbial assimilation and mineralization, itself constrained by land-use decisions and climate. Although microbes are the catalyst that controls soil carbon fate, the mechanisms that promote carbon persistence remain poorly described. This knowledge gap limits our ability to predict how land-use decisions contribute to climate change, and predictions of soil carbon in global models remain highly variable as a result. We have employed isotopic methods coupled to high throughput DNA sequencing to map the microbial mechanisms that govern the fate of soil carbon. We find that carbon assimilation dynamics can be used to identify microbial groups with distinct life history strategies, and we have identified genomic traits that predict microbial ecological strategies. We find that these microbial strategies constrain variation in carbon dynamics across soils that differ in land-use.
May 2, 2023: (4PM) – Patrick Shafto, Rutgers University
Belief Transport: The Mathematical Theory of Learning Agents.
Host: Ben Weiner
Increasingly humans and machines are integrated in decision making processes. Yet, we do not have a theory to guide construction or deployment. We will present a new mathematical framework – Belief Transport – that unifies different problems learning agents face, and present a detailed analysis of cooperative communication, a central aspect of social reasoning. We will prove mathematical properties, make connections to advances in optimization, and analyze implications for theories of human behavior. We will conclude with open questions, current directions, and potential implications.
May 17, 2023: (Note: This is a Wednesday seminar – 4PM) – Xaq Pitkow, Rice University
Inferring Inference.
Host: Ben Weiner
Repeating patterns of microcircuitry in the cerebral cortex suggest that the brain reuses elementary or “canonical” computations. Neural representations, however, are distributed, so the relevant operations may only be related indirectly to single-neuron transformations. It thus remains an open challenge how to define these canonical computations. We present a theory-driven mathematical framework for inferring implicit canonical computations from large-scale neural measurements. This work is motivated by one important class of cortical computation, probabilistic inference. We posit that the brain has a structured internal model of the world, and that it approximates probabilistic inference on this model using nonlinear message-passing implemented by recurrently connected neural population codes. Our general analysis method simultaneously finds (i) the neural representation of relevant variables, (ii) interactions between these latent variables that define the brain’s internal model of the world, and (iii) canonical message-functions that specify the implicit computations. With enough data, these properties are statistically distinguishable due to the symmetries inherent in any canonical computation, up to a global transformation of all interactions. As a concrete demonstration of this framework, we analyze artificial neural recordings generated by a model brain that implicitly implements advanced mean-field inference. Analysis of these models reveal certain features of experiment design required to successfully extract canonical computations from neural data.
May 23, 2023: ( 4PM) – Yun S. Song, University of California, Berkeley
Predeicting the Impact Of Mutations On Protein Synthesis And Function.
Host: Ben Weiner
Proteins are the workhorses of the cell and are involved in all aspects of cellular processes. In spite of notable technological advances in protein biology and genomics over the past decade, it remains an important challenge to unravel how protein synthesis and function are affected by genetic mutations. In this talk, I will describe my lab’s recent progress in tackling this challenge by leveraging new theoretical results on interacting particle systems and recent advances in machine learning.
September 12, 2023: (4PM) – Benjamin Weiner, The Rockefeller University
Plant-Pathogen Interactions And The Climate-Agriculture Nexus.
Host: Bertrand Ottino-Loffler
Like all multicellular organisms, plants have an immune system which protects them from pests and pathogens. Understanding the plant immune system is important from the standpoint of both basic biology and global food security, as experts estimate that 20-30% of global yields for important crops are lost to disease. Many of the genes involved in immunity have been identified, and we have begun to measure how the relevant proteins interact. However, we lack a framework for how the molecular interactions give rise to a functioning immune system. This hampers our understanding of plant-pathogen coevolution and impedes attempts to engineer more resilient crops. For example, although several sensing architectures have been identified (“direct recognition,” “guard-guardee,” “guard-decoy,” etc.), it remains unclear what evolutionary forces maintain this diversity. Here we find that a simple model based on molecular interactions explains a broad range of experimental observations. Nonlinear feedback leads to sharp immune activation, and the mystery of “effector interference” is shown to be a natural consequence of molecules competing for binding partners. We find that different sensing architectures obey functional trade-offs between complexity, sensitivity, and antivirulence, and we predict how the broad-spectrum defense gene ZAR1 will respond to multiple attacks at once. Finally, we contextualize these results within the broader nexus of climate and agriculture.
September 26, 2023: (4PM – Special Seminar) – Surya Ganguli, Stanford University
From Explainable AI To Neuroscience: Revealing The Emergence Of Computations From The Collective Dynamics Of Interacting Neurons.
Host: Eric Siggia
Concomitant advances in experimental neuroscience and deep learning now enable us to simultaneously observe the activity of many neurons, and then quantitatively describe their dynamics with highly accurate but complex models. But are we then merely replacing something we don’t understand (the brain) with something else we don’t understand (our complex model of it)? How can we instead derive a conceptual understanding of how important computations emerge from the model, as well as derive incisive experimental tests of the model? We will show how to use ideas from explainable AI and applied mathematics to address these questions in a range of systems from vision to navigation to AI systems. In particular, we will explain how the first steps of vision unfold in a single retinal circuit model that can capture over 2 decades of seminal experiments, including natural scene responses. Also we will explain simple principles governing how neural circuits can fuse information from landmarks and self-motion to create stable spatial maps of the world after a single exposure to a novel environment; our explainable model can predict detailed properties of entorhinal grid cell representations in new environments before a mouse even enters them.
October 10, 2023: (4PM) – Grace Lee, University of California, Irvine
How Transposable Elements Shape Genome Evolution Through Epigenetic Mechanisms.
Host: Li Zhao
Transposable elements (TEs) are widespread genome parasites whose presence is tightly intertwined with the evolution of their host genomes. Fifty years of transposon research primarily focused on how they break or change DNA sequences (“genetic effects”). Yet, growing evidence has suggested another important mechanism by which TEs impact genome function and evolution—through epigenetic silencing. Eukaryotic hosts typically silence TEs through the deposition of repressive epigenetic marks. While this silencing effect would reduce the replicative potential of TEs and should be beneficial to the hosts, our recent work found that epigenetic silencing of TEs also inadvertently results in harmful epigenetic effects both along the linear DNA and in 3D nuclear space. By using Drosophila as a model system, we identified genome-wide that repressive epigenetic marks at silenced euchromatic TEs spread to adjacent sequences. This spreading effect changes the epigenetic states of neighboring genes, tampers gene expression, and even modifies local recombination landscape. Silenced euchromatic TEs also spatially interact with distant pericentromeric heterochromatin, leading to altered 3D genome organization. Importantly, population genomic analysis identified selection against these TE-mediated epigenetic effects, and, across Drosophila species, the strength of TE-mediated epigenetic effects associates with genomic TE abundance. These observed functional and evolutionary consequences indicate that TE-mediated epigenetic effect is not only crucial for the evolutionary dynamics of TEs, but also a significant contributor to host genome evolution.
October 17, 2023: (4PM – Special Seminar) – Shaul Druckmann, Stanford University
Relating Circuit Dynamics To Computation: Robustness And Dimension-Specific Computation In Cortical Dynamics.
Host: Eric Siggia
Neural dynamics represent the hard-to-interpret substrate of circuit computations. Advances in large-scale recordings have highlighted the sheer spatiotemporal complexity of circuit dynamics within and across circuits, portraying in detail the difficulty of interpreting such dynamics and relating it to computation. Indeed, even in extremely simplified experimental conditions, one observes high-dimensional temporal dynamics in the relevant circuits. This complexity can be potentially addressed by the notion that not all changes in population activity have equal meaning, i.e., a small change in the evolution of activity along a particular dimension may have a bigger effect on a given computation than a large change in another. We term such conditions dimension-specific computation. If the brain operates under such conditions, our chances of being able to learn what computations a circuit is performing from observing its activity will be greatly improved.
Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Finally, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering.
Considering motor preparatory activity in a delayed response task we utilized neural recordings performed simultaneously with optogenetic perturbations to probe circuit dynamics. First, we revealed a remarkable robustness in the detailed evolution of certain dimensions of the population activity, beyond what was thought to be the case experimentally and theoretically. Second, the robust dimension in activity space carries nearly all of the decodable behavioral information whereas other non-robust dimensions contained nearly no decodable information, as if the circuit was setup to make informative dimensions stiff, i.e., resistive to perturbations, leaving uninformative dimensions sloppy, i.e., sensitive to perturbations. Finally, we show that this robustness can be achieved by a modular organization of circuitry, whereby modules whose dynamics normally evolve independently can correct each other’s dynamics when an individual module is perturbed, a common design feature in robust systems engineering.
October 24, 2023: (4PM – Special Seminar) – Massimo Vergassola, Ecole Normale Superieure
Biological Navigation Of Complex Environments.
Host: Eric Siggia
Living systems face the challenging task of navigating complex natural environments. Notable examples include long-distance orientation using airborne olfactory cues transported by turbulent winds, the tracking of surface-bound trails of odor cues, and flight in the lowest layers of the atmosphere. Terrestrial animals, insects, and birds have evolved navigation strategies that accomplish the above tasks with an efficiency that is often yet unmatched by human technology. Indeed, robotic applications for olfactory sniffers and unmanned aerial vehicles face similar challenges for the automated location of explosives, chemical, and toxic leaks, as well as the monitoring of biodiversity, surveillance, disaster relief, cargo relief, and agriculture. I shall review the above natural phenomena, discuss how neurobiology and physics shape and constrain navigation tasks, the role that machine learning methods can play in the field, and conclude with open issues and perspectives.
October 31, 2023: (4PM – Special Seminar) – Christoph Haselwandter, University of Southern California
Emergent Functional Structures In Cell Membranes.
Host: Rod MacKinnon
Over the past two decades, structural biology has provided enormous insight into the shape of membrane proteins, while experiments increasingly provide quantitative data on the structural organization of cell membranes from molecular length scales to length scales of hundreds of nanometers, revealing biological structure and function beyond single molecules. How do such new, biologically functional structures emerge from molecular interactions, and what key molecular properties do these structures depend on? We consider here this question in the context of five distinct model systems: Piezo ion channels, clathrin lattices, caveolae, emerin nanodomains, and bacterial vesicles. We show that theoretical physics can explain quantitatively how the biological properties of collections of molecules arise from molecular interactions, providing us with a way to separate biologically relevant and irrelevant molecular features. Focusing on Piezo ion channels, we demonstrate how Piezo’s ability to respond to mechanical force emerges from the joint shape and mechanical properties of the Piezo-lipid bilayer system. Our results show that Piezo’s structure and function are inextricably tied to the cell membrane environment and composition through bending elastic forces. We suggest how these insights can be leveraged to understand and predict molecular mechanisms regulating the sensation of touch and other biological processes mediated by collections of molecules.
November 7, 2023: (4PM) – Paul Francois, University of Montreal
T Cell Recognition: From Theory To Immunotherapy.
Host: Eric Siggia
Complex systems theory has taught us that simple, higher-level laws with few effective parameters can emerge from the interaction of small-scale components. As biology is becoming more and more quantitative, one can use a combination of first-principle theoretical modeling with machine learning techniques to build accurate and tractable theories of biological dynamics. Those dynamics can often be best understood in (abstract) latent spaces, giving « physics-like » intuition, interpretability and eventually allowing for new predictions and applications. I will illustrate the power of such approaches on the dynamics of the adaptive immune system, in particular T cell response. We used a robotic platform combined with machine learning to uncover a ‘Universal encoding’ from cytokine dynamics, and I will show how this response structure fits parsimonious models of immune recognition. Our approach suggests a new ‘antagonistic’ strategy for cancer immunotherapy that we validated both in vitro and in vivo.
November 14, 2023: (4PM) – Eric Dufresne, Cornell University
Living Droplets Get To Work.
Host: Bertrand Ottino-Loffler
Our understanding of the molecular driving forces that underly biomolecular condensation has rapidly matured. At the same time, we know relatively little about how emergent properties of condensates affect cellular physiology. In this talk, I will describe how capillary forces, which derive from the interface of condensates, can be leveraged to drive localization, deformation, and motility.
November 28, 2023: (4PM – Special Seminar) – Greg J. Stephens, Vrije University Amsterdam
A Markovian Dynamics For C. Elegans Behavior Across Scales.
Host: Marcelo Magnasco
How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm C. elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion, and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm’s foraging behavior, revealing both “runs-and-pirouettes” as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.
December 5, 2023: (4PM) – William M. Gelbart, University of California, Los Angeles
RNA vs DNA as Physical Objects: What’s Special About Viral Genomes?
Host: Eric Siggia
Viruses are exceptional among evolving species in that the majority of them have single-stranded (ss) RNA genomes. One big advantage of ssRNA over double-stranded (ds) DNA is that it is a much more compact way of encoding genetic information. Not only do viruses have orders of magnitude fewer genes than do living things, but each of their genes “takes up less space” than do DNA genes, allowing virus particles to be small enough so that 1000s of them can fit in a host cell. In my talk I discuss how the compactness of RNA genes allows for the spontaneous formation (self-assembly) of infectious virus particles from purified RNA and capsid protein, and how the possibility of in vitro reconstitution of virus-like particles — therapeutic mRNA encapsidated in a protein shell — provides a natural basis for new gene delivery platforms.
December 12, 2023: (4PM – Special Seminar) – David Sussillo, Stanford University
Flexible Multitask Computation In Recurrent Networks Utilizes Shared Dynamical Motifs.
Host: Eric Siggia
Flexible computation is a hallmark of intelligent behavior. Yet, little is known about how neural networks contextually reconfigure for different computations. Humans are able to perform a new task without extensive training, presumably through the composition of elementary processes that were previously learned. Cognitive scientists have long hypothesized the possibility of a compositional neural code, where complex neural computations are made up of constituent components; however, the neural substrate underlying this structure remains elusive in biological and artificial neural networks. Here we identified an algorithmic neural substrate for compositional computation through the study of multitasking artificial recurrent neural networks. Dynamical systems analyses of networks revealed learned computational strategies that mirrored the modular subtask structure of the task-set used for training. Dynamical motifs such as attractors, decision boundaries and rotations were reused across different task computations. In summary, we present a conceptual framework that establishes dynamical motifs as a fundamental unit of computation, intermediate between the neuron and the network. As more whole brain imaging studies record neural activity from multiple specialized systems simultaneously, the framework of dynamical motifs will guide questions about specialization and generalization across brain regions.
December 19, 2023: (4PM – Special Seminar) – Ila Fiete, Massachusetts Institute of Technology
Modeling The Emergence Of Complex Cortical Structure From Simple Precursors In The Brain: Maps, Hierarchies, And Modules.
Host: Eric Siggia
Modular and hierarchical structures are ubiquitous in the brain. Two distinct hypotheses for such morphogenesis involve genetic specification (the positional information hypothesis) or spontaneous structure emergence from symmetry breaking (the pattern formation hypothesis). Indeed, there is rich evidence supporting both hypotheses in different systems, and more recently evidence that both systems might interact, for instance with genetic specification providing an initial but relatively low-information scaffold of positional guidance and pattern formation constructing sharper structures by bootstrapping from this guidance. In this talk, I will consider the emergence of two systems in the brain: the visual processing hierarchy with topographic structure, and a modular cognitive circuit consisting of functionally independent grid cell networks that compute spatial location from velocity cues as animals move and navigate the world. I will describe how simple activity-driven growth and competition rules can lead to the emergence of topographically ordered sensory processing hierarchies, and how genetically specified smooth gradients with purely local recurrent interactions on two scales can lead to global module emergence. In sum, simple growth rules, local interactions and smooth gradients can interact to produce rich emergent order on multiple scales in the form of maps, modules, and hierarchies.
January 4, 2022: (Special Time: 10AM via Zoom Only, Physics Fellow Candidate) – David Hathcock, Cornell University
Universal Absorption-Time Distributions In Evolutionary Dynamics And Epidemics.
Host: Bertrand Ottino-Loffler
Stochastic models in evolutionary biology and epidemiology often consist of birth-death dynamics where absorption times are the key quantity of interest: how long does it take for an advantageous mutation to become fixed or for an epidemic to subside? In this talk I will discuss our recent efforts to classify universal absorption-time distributions for birth-death Markov chains with an absorbing boundary state. Based on generic features of the transition rates, the asymptotic distribution for “extinction-prone” chains is either Gaussian, Gumbel, or a convolution of Gumbel distributions. In particular, the distribution is Gaussian if the transition rates are sufficiently uniform. Conversely, the later cases are closely related to extreme value theory: the Gumbel distribution emerges due to extremal events dominating the absorption process. Our classification applies to simple birth-death models of evolution, ecology, and epidemiology, but also captures the essential features of more complicated systems. For example, the distribution of times to eradicate African sleeping sickness, recently predicted using a high-dimensional epidemiological model, closely resembles the Gumbel distribution.
January 6, 2022: (Special Time: Noon via Zoom Only, Physics Fellow Candidate) – Nicholas Lammers, University of California, Berkeley
Uncovering The Kinetic Fingerprints Of Transcriptional Control Using Gene Expression Dynamics.
Host: Jasmine Nirody
Gene regulation is central to cellular function. Yet, despite decades of biochemical and genetic studies that have established a reasonably complete “parts list” of the molecular components required for eukaryotic transcription, we nonetheless lack quantitative models that can predict how these pieces interact in space and time to give rise to robust gene regulatory logic. For this talk, I will survey three distinct, yet interrelated projects from my Ph.D. that combine live imaging, computational methods, and theoretical modeling to dissect the molecular underpinnings of transcriptional control in the developing fruit fly embryo. To begin, I discuss results from a project that utilizes a novel statistical technique and live single-cell measurements of transcription to uncover how transcription factors modulate the kinetics of the transcriptional cycle to produce a sharp stripe of gene expression. Next, I share recent results that utilize cutting-edge optogenetic methods to rapidly export repressor proteins from cells, revealing that transcriptional repression—and the development trajectories it dictates—is rapidly reversible. To close, I outline ongoing theoretical work that moves beyond phenomenological models of transcription to consider a molecular picture of how transcription factor binding transmits information to drive cellular decisions. These calculations reveal that non-equilibrium gene regulatory mechanisms, which require the expenditure of biochemical energy, may be necessary in order for gene loci to function in the context of crowded cellular environments.
January 13, 2022: (Special Time: Noon via Zoom Only, Physics Fellow Candidate) – Guruprasad Raghavan, California Institute of Technology
Engineering Flexible Machine Learning Systems Inspired By Biological Intelligence.
Host: Liat Shenhav
AI systems have achieved human performance on tasks ranging from image recognition to game playing. However, they lack flexibility, a key component of intelligence. In this talk, I will focus on two facets of flexibility lacking in deep networks and will present insights from biological intelligence and differential geometry to build next-gen AI. Deep networks require laborious re-engineering, as they rely on human-designed architectures, for processing static, dynamic visual inputs from a wide range of sensor geometries. Inspired by the development of neural circuits, I present a bio-inspired algorithm that uses spontaneous spatiotemporal activity waves to grow and self-organize spiking networks on arbitrary 2D/3D sensor geometries and can internalize dynamic input representations. Additionally, deep networks cannot flexibly modify their architecture while maintaining task performance, succumbing to catastrophic forgetting (CF) while learning sequential tasks. However, humans and animals have flexible neural circuits that aren’t subject to CF. To understand principles underlying architectural flexibility, I present a geometric framework that discovers sets of networks with near equivalent functional performance on a specific task, enabling networks to preserve previous information while learning new tasks. Finally, I will present some recent work extending these frameworks to grow systems with multiple brain-regions to simulate a ‘Theory of Mind’ and enable cognitive flexibility .
January 18, 2022: (Special Time: 10AM via Zoom Only, Physics Fellow Candidate) – Sean Fancher, University of Pennsylvania
Stochastic Network Theory: Precision, Robustness, and Information Flow within Developing and Dynamic Systems.
Host: Ben Weiner
Transmission of material and information between cells is a key component of any multicellular organism. This broad class of processes manifests in a multitude of different forms at various stages of the organism’s life. In this talk, I will explore two particular examples of such material and information transmission: the distribution of morphogen molecules within developing systems and the dynamics of blood flow through the animal vasculature. In the case of the former, morphogens are particular molecular species that cells use to decode their position within the organism and thus what cell types to differentiate into. By examining two distinct models of how these molecules are distributed from their source, I will show that there exists a significant difference in the precision with which these models overcome the inherently stochastic nature of cellular processes. In the case of the latter, the blood vasculature forms a complex network of vessels that are each compliant and capable of deforming in response to changes in blood pressure. I will show how this compliance affects the speed at which information can be mechanically transmitted throughout the network by examining network topology and extrapolating analytically calculable properties of single vessels. Finally, I will discuss the basis of Stochastic Network Theory, wherein I apply stochastic dynamics to network structures, and explore its potential applications to the study of the cytoneme network model of morphogen transport.
February 15, 2022: (4PM) – Francis Corson, Ecole Normale Superieure
Mechanics Of Embryonic Self-Organization.
Host: Eric Siggia
Mechanical forces play an essential role in development, most evidently as the drivers of morphogenesis, but also potentially as long-range signals contributing to embryonic self-organization. Regulative development is particularly evident in experiments in which the avian embryonic disk is bisected: each half can give rise to a fully-formed embryo, implying a dramatic redirection of force generation and gene expression. Having identified a contractile ring, at the boundary between the embryonic and extraembryonic territories, as the engine of early avian morphogenesis, we wondered whether tension along the embryo margin might underlie embryonic regulation. Indeed, a mechanical analog of a Turing model, in which contractility plays the role of activator, and tension the role of inhibitor, recapitulates the steady pattern of tissue motion in intact embryos and its redirection upon bisection. We further show that mechanical feedback also impinges on gene expression, driving the emergence of ectopic embryos and the accompanying rescaling of embryonic territories. Our findings demonstrate a central role for mechanical forces in embryonic self-organization and cell fate allocation.
March 8, 2022: (4PM) – Stanislav Y. Shvartsman, Princeton University
Developmental Effects Of Mutations In Signaling Systems.
Host: Eric Siggia
Gain-of-function mutations affecting the highly conserved Ras cascade are associated with a range of developmental abnormalities, including heart defects, stunted growth, and neurocognitive deficits. Sequencing of affected individuals identified multiple variants in well-studied proteins, but their causal connections to the emerging phenotypes remain to be established. This is critical for our fundamental understanding of developmental diseases and requires robust approaches for following large cohorts of developing organisms with carefully controlled genotypes. We used CRISPR/Cas9 gene editing to realize such an approach in Drosophila, which offers numerous advantages for investigating organismal effects of deregulated signaling. Our analysis of developmental progression in gene-edited flies revealed that mutations compatible with development can nevertheless drastically reduce the probability of reaching adulthood in affected individuals. This quantitative result sheds light on the phenotypic heterogeneity of developmental defects caused by mutations in signaling systems.
March 29, 2022: (4PM) – Gregoire Altan-Bonnet, NCI, NIH
Universal Antigen Encoding Of T Cell Activation From High Dimensional Cytokine Dynamics.
Host: Eric Siggia
Systems Immunology lacks a framework to derive theoretical understanding from high-dimensional datasets. We combined a robotic platform with machine learning to experimentally measure and theoretically model CD8+ T cell activation. High-dimensional cytokine dynamics could be compressed onto a low-dimensional latent space in an antigen-specific manner (so-called “antigen encoding”). We used antigen encoding to model and reconstruct patterns of T cell immune activation. The model delineated 6 classes of antigens eliciting distinct T cell responses. We generalized antigen encoding to multiple immune settings, including drug perturbations and activation of chimeric antigen receptor T cells. Such universal antigen encoding for T cell activation may enable further modeling of immune responses and their rational manipulation to optimize immunotherapies. [Collaboration with Paul François’s group @ McGill – Manuscript (Science, in press): available on demand]
April 5, 2022: (4PM) – Ben Machta, Yale University
Criticality And Dynamical Bifurcations For Signal Processing And Amplification .
Host: Ben Weiner
Critical points are special places in parameter space where macroscopic system behavior is particularly sensitive to small changes in molecular details. In this talk I will explore two mechanisms through which cells could use a critical point and a dynamical bifurcation, respectively, to sensitively amplify and integrate information arriving at many individually noisy receptors.
First I will discuss the pit organ of certain snakes which forms a low resolution infrared image used for hunting prey. For this organ to be functional, individual neurons innervating the pit must be able to respond to local heating <1mk, at least 1000x more sensitive than the thermo-TRP ion channels which act as molecular receptors. I will present a model for how this remarkable sensitivity can be achieved. In this model, individual channels are electrically coupled, self-tuning to a bifurcation separating a mostly silent ‘irregular spiking’ state from an active ‘regular spiking’ state. This coupling effectively integrates an order one fraction of the information available in individual channels into the cooperative output of spike frequency.
Second I will discuss our recent work to understand the role of liquid-liquid criticality in signal transduction in eukaryotic cells. This work is motivated by two complementary experimental findings – first, that cellular membranes are two dimensional liquids tuned near a miscibility critical point, and second that many signal cascades begin with the formation of signaling cluster – a membrane domain enriched in particular lipids and membrane bound proteins under which bulk proteins with roles in signaling aggregate. I will argue that these platforms are examples of what we term surface densities; a two-dimensional prewet phase held together by critical casimir forces in the membrane and weak protein-protein interactions that reflect the propensity of many bulk proteins to phase separate. The liquid environment in these surface phases enables certain chemical reactions that are important in the early stages of signaling. As a result, the formation of a prewet phase acts as a crucial step for integrating a signal.
April 19, 2022: (4PM – Via Zoom Only) – Bjorn Sandstede, Brown University
Agent-Based Modeling And Topological Data Analysis Of Zebrafish Patterns.
Host: Bertrand Ottino-Loffler
Patterns are widespread in nature and often form during early development due to the self-organization of cells or other independent agents. One example are zebrafish (Danio rerio): wild-type zebrafish have regular black and gold stripes, while mutants and other fish feature spotty and patchy patterns. Qualitatively, these patterns display impressive consistency and redundancy, yet variability inevitably exists on both microscopic and macroscopic scales. I will first discuss an agent-based model that suggests that both consistency and richness of patterning on zebrafish stems from the presence of redundancy in iridophore interactions. In the second part of my talk, I will focus on how we can quantify features and variability of patterns to facilitate predictive analyses. I will discuss an approach based on topological data analysis for quantifying both agent-level features and global pattern attributes on a large scale. The proposed methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns and predicts stripe and spot statistics as a function of varying cellular communication. This is joint work with Alexandria Volkening and Melissa McGuirl.
April 26, 2022: (4PM) – Steven Strogatz, Cornell University
The Math And Science Of Getting In Sync.
Host: Bertrand Ottino-Loffler
Every night along the tidal rivers of Malaysia, thousands of male fireflies congregate in the mangrove trees and flash on and off in unison. Similar feats of synchronization occur throughout the natural world and in our own bodies, as well as in many kinds of physical systems, ranging from pendulum clocks and metronomes to lasers and Josephson junctions. In the first part of this talk, I’ll provide an introduction to the math and science of collective synchronization. After that I’ll discuss some exciting new results and unsolved problems about how the topology of a network affects its tendency to synchronize.
May 3, 2022: (4PM) – Oded Rechavi, Tel Aviv University
Rethinking Inheritance (Of Acquired Traits).
Host: Liat Shenhav
In C. elegans nematodes, dedicated machinery enables transmission of small RNAs which regulate gene expression across multiple generations, independently of changes to the DNA sequence. Different environmental challenges, including exposure to starvation, pathogens, and heat stress generate heritable small RNA responses, that in certain cases can be adaptive. Recently we have also shown that even neuronal activity can produce small RNA-mediated heritable responses, and that the decisions that the progeny makes are affected by whether their ancestors experienced stress or not. I will discuss the underlying mechanisms, and the potential of small RNA inheritance to affect the worms’ fate. Lastly, we will examine how these new findings might affect our view of the process of evolution and the limits of inheritance.
May 10, 2022: (4PM) – Paul Francois, McGill University
Clock, Wave, Entrainment For Vertebrate Segmentation.
Host: Eric Siggia
Vertebrae precursors (called somites) sequentially form during embryonic development under the control of coupled cellular oscillators. Waves of genetic expressions (associated to Notch and Wnt signaling pathways) sweep the growing embryo from posterior to anterior, controlling somite patterning and their segregation. This complex process is highly conserved at the phenotypic level, but different genes oscillate in different species, which presents a major challenge for its understanding. I will describe how we built higher level, phenotypic models to describe vertebrate segmentation. Advances in experimental techniques further allow us to monitor and control those oscillations, leading to insights into the geometric properties of the oscillators implicated. In particular I will describe how the “segmentation clock” can be entrained and how tail bud cells synchronize.
May 24, 2022: (4PM) – Jean-Pierre Eckmann, University of Geneva
The Predictive Power Of Theoretical Biology: An Example.
Host: Eric Siggia
I will argue, supported by an experiment, that theoretical biology moves (very slowly) in a direction where predictions (as in theoretical physics) become possible. Starting from insights for a simple mechanical-genetic model of protein and the interpretation of its spectral properties, one can formulate some predictions on the presence or absence of an effect when the protein is mutated. The results suggest that only mutations at specific positions in the gene sequence have a critical effect on the function. These predictions have then been checked in a delicate experiment with actual mutations in the protein Guanylate kinase. A clear signal confirms the theoretical prediction. Some of the people involved in this work are Tsvi Tlusty, Marta Siek, John McBride, Jacques Rougemont, Elisha Moses, Eyal Weinreb, Albert Libchaber, and Stan Leibler.
September 13, 2022: (4PM) – Massimo Vergassola, Ecole Normale Superieure
Biomimetic Navigation of Complex Natural Environments.
Host: Eric Siggia
Living systems face the challenge of navigating natural environments shaped by non-trivial physical mechanisms. Notable examples are provided by long-distance orientation using airborne olfactory cues transported by turbulent flow, the tracking of surface-bound trails of odor cues, and flight in the lowest layers of the atmosphere. Terrestrial animals, insects, and birds have evolved navigation strategies that accomplish the above tasks with an efficiency that is often surprising and yet unmatched by human technology. Indeed, robotic applications for olfactory sniffers and unmanned aerial vehicles face similar challenges for the automated location of explosives, chemical, and toxic leaks, as well as the monitoring of biodiversity, surveillance, disaster relief, cargo transport, and agriculture. The interdisciplinary interplay between biology, physics, and robotics is key to jointly advancing fundamental understanding and technology. I shall review the above natural phenomena, then discuss the physics that constrains and shapes the navigation tasks, how machine-learning methods are brought to bear on those tasks and conclude with the relevant strategies of behavior and open issues.
September 29, 2022: (Note Special Day: Thursday – 4PM) – Tzachi Pilpel, Weizmann Institute of Science
A Search for Evolvability Affecting Genes and Mechanisms.
Host: Liat Shenhav
Evolvability – the capacity to evolve, must have evolved itself. Which genes confer evolvability upon the organism, and are there “conservatism” genes, the restrain evolvability? One key determinant of capacity and rate of evolution is the rate of mutation. Organisms face a tradeoff, too low mutation rate prevents adaptation, while too high might compromise the parts of the genome that “work” already. But mutation rate needs not be constant. It can vary between environmental conditions, e.g. featuring stress induced mutagenesis, between genes, or between individual cells in the population. Each of these dimensions of variability in mutation rate may affect evolavbility in different ways. I’ll present our experiments into reverse transcription in yeast as a means to achieve gene-specific mutation rate, and a theoretical study that suggests that stochastic variation in mutation rate among isogenic cells may facilitate evolution.
I’ll present a systematic screen for evolability affecting genes in yeast.
Another major determinant of evolvability is recombination. I’ll present our yeast barcoding system that allows en mass mating assay between hundreds of yeast strains. The study suggests mechanisms that allow to chose mating partners that optimize offspring fitness. In parallel I’ll present our experimental evolution study that showed effects of horizontal gene transfer on rate of bacterial adaptation.
Lastly, I’ll present the known concept of genetic assimilation – the process in which phenotypic variation precedes genetic mutation. I’ll expose the possibility that translation errors may pave the evolutionary road to genetic mutations.
Together these studies suggest a range of molecular and cellular mechanisms that facilitate evolution.
I’ll present a systematic screen for evolability affecting genes in yeast.
Another major determinant of evolvability is recombination. I’ll present our yeast barcoding system that allows en mass mating assay between hundreds of yeast strains. The study suggests mechanisms that allow to chose mating partners that optimize offspring fitness. In parallel I’ll present our experimental evolution study that showed effects of horizontal gene transfer on rate of bacterial adaptation.
Lastly, I’ll present the known concept of genetic assimilation – the process in which phenotypic variation precedes genetic mutation. I’ll expose the possibility that translation errors may pave the evolutionary road to genetic mutations.
Together these studies suggest a range of molecular and cellular mechanisms that facilitate evolution.
October 11, 2022: (4PM) – William Bialek, Princeton University
Searching for Scale Invariance in Neurons and Behavior.
Host: Eric Siggia
Many biological systems exhibit dynamics that span a wide range of scales. One possibility is that there are many distinct processes, one for each discrete scale. Another possibility is that interactions among many internal degrees of freedom generate a near continuum of scales. By analogy with physics problems that we understand quite well, the emergence of such scale invariance would point to some deeper underlying theory. But searching for scale invariance is subtle. In animal behavior, correlations that extend over long times are tangled with individual differences. In networks of neurons, the dense connectivity can make it difficult even to say what we mean by scale. I will discuss new approaches to these problems in the analysis of experiments on the behavior of walking flies and the patterns of neural activity in the mouse hippocampus. The results provide evidence for precise scale invariance over several decades, with scaling exponents that are reproducible, sometimes to the second decimal place.
November 1, 2022: (4PM) – CANCELLED: Arup Chakraborty, Massachusetts Institute of Technology
The Antibody Response to Mutable Viruses.
Host: Bertrand Ottino-Loffler
Infectious disease-causing pathogens have plagued humanity since antiquity, and the COVID-19 pandemic has been a vivid reminder of this perpetual existential threat. Vaccination has saved more lives than any other medical procedure, and indeed, effective vaccines have helped control the COVID-19 pandemic. However, we do not have effective vaccines against rapidly mutating viruses, such as HIV; nor do we have a universal vaccine against seasonal variants of influenza or SARS-CoV-2 variants that continue to evolve. The ability to develop effective universal vaccines that protect us from variant strains of mutable viruses will help create a more pandemic-resilient world. In this talk, I will describe how by bringing together approaches from statistical physics, virology and immunology, progress is being made to address this challenge. In particular, I will focus on approaches that aim to design vaccines and immunization strategies that elicit antibodies that can protect against diverse mutant strains. As I hope to show, this is a problem at the intersection of statistical physics, evolutionary biology, immunology, and vaccine development. The application of fundamental concepts to HIV, influenza and SARS-CoV-2 vaccines will be discussed.
November 8, 2022: (4PM – Via Zoom Only) – Nigel Goldenfeld, University of California, San Diego
Topological Scaling Laws And The Statistical Mechanics Of Evolution .
Host: Ben Weiner
For the last 3.8 billion years, the large-scale structure of evolution has followed a pattern of speciation that can be described by branching trees. Recent work, especially on bacterial sequences, has established that despite their apparent complexity, these so-called phylogenetic or evolutionary trees exhibit two unexplained broad structural features which are consistent across evolutionary time. The first is that phylogenetic trees exhibit scale-invariant topology, which quantifies the fact that their branching lies in between the two extreme cases of balanced binary trees and maximally unbalanced ones. The second is that the backbones of phylogenetic trees exhibit bursts of diversification on all timescales. I present a coarse-grained statistical mechanics model of ecological niche construction coupled to a simple model of speciation, and use renormalization group arguments to show that the statistical scaling properties of the resultant phylogenetic trees recapitulate both the scale-invariant topology and the bursty pattern of diversification in time. These results show in principle how dynamical scaling laws of phylogenetic trees on long time-scales may emerge from generic aspects of the interplay between ecological and evolutionary processes, leading to scale interference.
Finally, I will argue that these sorts of simplistic, minimal arguments might have a place in understanding other large-scale aspects of evolutionary biology. In particular I will mention two questions where we do not have even a qualitative understanding let alone a quantitative one: (1) the spontaneous emergence of the open-ended growth of complexity; (2) the response of evolving systems to perturbations and the implications for their control. Even though biology is intimidatingly complex, “everything has an exception”, and there are a huge number of undetermined parameters, statistical physics reasoning may lead to useful new insights into the existence and universal characteristics of living systems.
Work performed in collaboration with Chi Xue and Zhiru Liu and supported by NASA through co-operative agreement NNA13AA91A through the NASA Astrobiology Institute for Universal Biology.
November 15, 2022: (4PM) – Timothy Holy, Washington University School of Medicine at St. Louis
Biophysics Of Topographic Localization In Olfaction.
Host: Ben Weiner
Most organisms have three senses—vision, audition, and olfaction—capable of detecting stimuli at a distance. Vision and audition encode both “what” and “where,” and the neural mechanisms for topographic detection are well-established. Recent data suggest that spatial topography is also relevant for “near-field olfaction,” and that fluid flow in the mouse nasal cavity preserves streamlines. The computations needed for odor localization may resolve some long-standing mysteries about the anatomy of neural circuitry in the mammalian nervous system.
November 22, 2022: (4PM) – Petr Sulc, Arizona State University
Multiscale Modeling Of Nucleic Acids For Self-Assembled Biomaterials And Aptamer Design.
Host: Ben Weiner
One of the main tasks in bionanotechnology is to design interactions between building blocks such that they self-assemble in high yield into a target desired structure while avoiding undesired alternative assemblies. Here, we present a new framework. The method can efficiently search the space of possible solutions in terms of distinct particles and interaction types to find a minimum complexity solution which can reliably self-assemble into a target structure and avoid unwanted competing states. We apply this method to the design of highly sought-after 3D lattices that include the tetrastack (pyrochlore) and cubic diamond lattice, which have promising applications in biotemplated metamaterial design. In the second part of the talk, we will discuss use of machine learning tools trained on SELEX datasets to generate nucleic acid binders (aptamers) that specifically bind to thrombin and SARS-CoV-2 spike protein, and will present a novel nanotechnology platform to create multivalent specific binders against target proteins.
November 29, 2022: (4PM – Via Zoom Only) – Giovanni Zocchi, University of California, Los Angeles
Cell-Free Action Potentials: The Artificial Axon.
Host: Bertrand Ottino-Loffler
Action potentials are the voltage spikes produced in the neuron. Their generation and regulation has been studied in living cells for close to 100 years. With the Artificial Axon, we take this most interesting of dynamical systems out of the cell. Our minimal system is based on biological molecular components: ion channels, lipid bilayers. It displays the basic electrophysiology of neurons: threshold firing, and integrate-and-fire dynamics. I will present the experimental system and describe some delicate measurements of critical behavior, difficult to obtain from real neurons. Next I will present an analysis of the corresponding space extended system. I will focus on the propagation of kinks or solitary waves, drawing an analogy with other kinks in condensed matter physics. I will then show that action potentials can propagate in the minimal system consisting of a single ion channel species, in the presence of channel inactivation.
December 13, 2022: (4PM) – Sahand Jamal Rahi, Ecole Polytechnique Federale de Lausanne
When Cells Decide To Give Up On Repairing DNA Damage.
Host: Ben Weiner
Why biological quality-control systems fail is often mysterious. Checkpoints in yeast and animals are overridden after prolonged arrests allowing self-replication to proceed despite the continued presence of errors. Although critical for biological systems, checkpoint override is not understood quantitatively or at the system level by experiment or theory. To uncover potential patterns obeyed by error correction systems, we derived the mathematically optimal checkpoint strategy, balancing the trade-off between risk and opportunities for growth. The theory predicts the optimal override time without free parameters based on two inputs, the statistics i) of error correction and ii) of survival. We applied the theory experimentally to the DNA damage checkpoint in budding yeast, an intensively researched model for other eukaryotes. Using a fluorescent reporter which allowed cells with DNA breaks to be isolated by flow cytometry, we quantified i) the probability distribution of repair for a double-strand DNA break (DSB) as well as ii) the survival probability after override. Based on these two measurements, the optimal checkpoint theory predicted remarkably accurately the DNA damage checkpoint override times as a function of DSB numbers, which we also measured for the first time precisely. Thus, a first-principles calculation uncovered hitherto hidden patterns underlying the highly noisy checkpoint override process. The universal nature of the balance between risk and self-replication opportunity is in principle relevant to many other systems, suggesting potential further applications.
January 4, 2021: (Special Time: 10AM via Zoom, Physics Fellow Candidate) – Purba Chatterjee, University of Illinois at Urbana-Champaign
The Non-Equilibrium Physics of Driven Living Matter.
Host: Archishman Raju
The emergence of macroscopic collective behavior from stochastic individual interactions of agents is ubiquitous across all length scales in biology. In this talk, I will discuss two instances of such emergent collective behavior. The first is flocking, the collective migration of many agents over length scales much larger than their individual sizes. Through a minimal model I show that while two-body interactions are insufficient, stochastic three-body interactions can recover the full phenomenological phase diagram for systems that flock at high densities. I also show that the intrinsic stochasticity gives rise to a new phase at low densities with just two body interactions. These results point to the uniqueness of biological active phases, which need descriptions that go beyond the framework of standard kinetic theories. The second example is the emergence of collective dynamics in RNA polymerases (RNAPs) during transcription. I show that a model of transcription regulation via DNA supercoiling can capture the experimentally observed switch from cooperative to antagonistic dynamics of RNAPs on promoter repression. Important to this mechanism is the hypothesis that transcription factors act as physical barriers to supercoil diffusion, which lays important groundwork for modelling transcription dynamics in the genomic context.
January 5, 2021: (4PM via Zoom, Physics Fellow Candidate) – Mitchell Newberry, University of Michigan
Theory as a Lens: Measuring Selection, Drift, Frequency Dependence and Power Laws in Big Data.
Host: David Zeevi
Does culture evolve according to Darwinian laws? Do blood vessel sizes follow a power-law distribution? Why does the order of genes on chromosomes persist for millions of years within a species but fluctuate widely between closely-related species? Can the same forces that shape diversity in tropical trees explain the diversity of first names? I weigh in on each of these questions by fitting models from theoretical biology to diverse real-world data. Along the way I correct estimates of severe earthquake frequency, propose a mechanism for fashions among dog breeders, and contemplate analogies between biodiversity conservation and the disappearance of rare languages.
January 7, 2021: (Special Time: 10AM via Zoom, Physics Fellow Candidate) – Ofer Kimchi, Harvard University
Biological Self-Assembly In and Out of Equilibrium.
Host: Jasmine Nirody
The application of statistical physics to biological systems has achieved remarkable successes over the past several decades. However, while statistical physics primarily excels at describing equilibrium systems, a hallmark of biological systems is their out-of-equilibrium nature. How can we build on equilibrium statistical physics to push forward into a greater understanding of non-equilibrium biological systems? I will describe my previous work and future interests in addressing this challenge, focusing especially on two related problems of biological self-assembly: the self-assembly of protein multimers, and nucleic acid hybridization. How can we improve the yields of de novo-designed multimeric protein complexes, and how can we build on that success to design protein-based dynamical systems? And: How can we build on our knowledge of the free energy landscapes of nucleic acid structures to predict non-equilibrium nucleic acid hybridization?
January 11, 2021: (Special Time: 10AM via Zoom, Physics Fellow Candidate) – Benjamin Weiner, Princeton University
Spatial Self-Organization, From Molecules To Microbes.
Host: Jasmine Nirody
Understanding how biological systems self-organize across spatial scales is a central problem in the physics of living matter. In this thesis, I address two paradigmatic examples: the organization of biomolecules in eukaryotic cells and the organization of organisms in an ecosystem. Eukaryotic cells are able to organize molecules into phase-separated condensates which lack a membrane. How do the thermodynamic and material properties of condensates emerge from the physical interactions of their components? We find that when phase separation is driven by specific, heterotypic interactions, condensates are governed by the entropy of self-interactions, a new mechanism for the biological control of intracellular phase separation. In the context of microbial ecology, many phenomenological models assume that spatial structure increases biodiversity. In contrast, we find that the physics of spatial competition for resources and territory reduces diversity. However, it also renders diversity robust to fitness differences and leads to multiple ecological stable states.
January 12, 2021: (4PM via Zoom, Physics Fellow Candidate) – Bertrand Ottino-Loffler, MIT
Evolutionary Dynamics of Incubation Periods.
Host: David Zeevi
The incubation period for typhoid, polio, measles, leukemia and many other diseases follows a right-skewed, approximately lognormal distribution. Although this pattern was discovered more than sixty years ago, it remains an open question to explain its ubiquity. Here, we propose an explanation based on evolutionary dynamics on graphs. For simple models of a mutant or pathogen invading a network-structured population of healthy cells, we show that skewed distributions of incubation periods emerge for a wide range of assumptions about invader fitness, competition dynamics, and network structure. The skewness stems from stochastic mechanisms associated with two classic problems in probability theory: the coupon collector and the random walk. Unlike previous explanations that rely crucially on heterogeneity, our results hold even for homogeneous populations. Thus, we predict that two equally healthy individuals subjected to equal doses of equally pathogenic agents may, by chance alone, show remarkably different time courses of disease.
January 14, 2021: (Special Time: 10AM via Zoom, Physics Fellow Candidate) – Trung Van Phan, Princeton University
E.coli in Complex Dynamical Environments and Robots on an Interactive Landscape.
Host: Liat Shenhav
Bacteria outside of the lab must often navigate complex environments in search of food. A better understanding of how bacteria find their way might help researchers develop strategies to inhibit bacterial infections. To that end, we probe to what extent the common bacteria E. coli explores landscapes that have much in common with structures experienced in nature—fractals and mazes—and find that they appear to have evolved a variety of ways to navigate these puzzles.
Bacteria communicate with each other via chemical signals, which carry information. Loss of information from other bacteria can be viewed as a disturbing event for a bacterium and a signal to avoid that region of space. We used hydrodynamic flow past a small aperture to remove bacterial signaling, with the expectation that bacteria would avoid that aperture due to an effective horizon that absorbed information: a “black hole”. We confirm that a bacterial “Hawking radiation” — collective bacterial waves launching away from the horizon — exists, even in the presence of strong geometrical pumping.
In the final part of the talk, we present an ecology-inspired form of active matter consisting of a robot swarm on an interactive light-emitting diode lightboard.
February 9, 2021: (4PM via Zoom) – Kenneth Welch, University of Toronto Scarborough
The Exceptional Flexibility of an Extreme Dietary Specialist: The Ruby-Throated Hummingbird.
Host: Jasmine Nirody
At just 3 or so grams, the ruby-throated hummingbird (Archilochus colubris) is one of the smallest vertebrates in North America. Owing to their small size and their use of hovering, hummingbirds exhibit metabolic rates and rates of daily energy expenditure that are among the highest for any vertebrate. It is no coincidence that these animals subsist almost exclusively on large amounts of floral nectar, a ready fuel source. But this simple concept of rapid energy intake to support rapid energy expenditure ignores the exceptional challenges that come both from the high sugar loads they experience, and from the need to achieve energy balance in the face of demands that vary dynamically at both daily and seasonal timescales. In this talk, I will present what my lab has learned about the unique ways in which hummingbird physiology and behavior have been shaped by their dietary ecology and need to manage energy homeostasis. Our work highlights the importance of specializations that both support elevated capacities for fuel mobilization and oxidation as well as that enable them to drastically reduce energy demand in a dynamic and flexible way.
February 16, 2021: (4PM via Zoom) – Maren Vitousek, Cornell University
Coping with Stress: From Integrative Mechanisms to Evolutionary Consequences.
Host: Jasmine Nirody
From a close encounter with a predator to a bout of severe weather, brief stressors are common in nature. Responding appropriately to these challenges can be crucial for survival. Yet why do individuals experiencing similar conditions often respond to challenges in markedly different ways? Why are some populations and species more stress resilient than others? In this talk I will discuss how taking an evolutionary approach to the mechanisms of behavior can help to answer these and other pressing questions. In vertebrates, glucocorticoid hormones are central mediators of behavior, particularly during major challenges. Research in my lab, using tree swallows as a model species, is showing that consistent and heritable differences in glucocorticoid regulation influence how individuals cope with challenges, and the fitness effects of stressors. I will also describe how large-scale phylogenetic comparative analyses have begun to illuminate how selection shapes stress responsiveness across vertebrates.
February 23, 2021: (4PM via Zoom) – Arnold Hayer, McGill University
Exploring Mechanisms Controlling Collective Cell Migration – From Local Signaling Events To Multicellular Organization.
Host: Jasmine Nirody
During collective migration of mammalian cells, cells use autonomous mechanisms for locomotion, while neighboring cells are physically connected to each other through adhesive cell-cell contacts. What are the biochemical and mechanical signals through which neighboring cells communicate with each other, enabling coordinated cell movement? My group investigates this problem in monolayers of cultured endothelial cells, in which patterns of coordinated cell movement emerge spontaneously, and where subcellular signaling events can be linked to multicellular behavior. Our primary focus is on the actin cytoskeleton, adhesion complexes, and their dynamic regulation by RhoGTPases. We use live-cell microscopy and fluorescence-based reporters to visualize key structures and signaling events, which are then linked to cell behavior using computational image analysis. During my talk, I will discuss recent findings on how VE-cadherin-mediated cell-cell contacts enable intercellular communication, and how RhoGTPase signaling is spatiotemporally regulated, both during autonomous and collective cell migration.
March 2, 2021: (4PM via Zoom) – Peter Andolfatto, Columbia University
Constraints On Adaptation Revealed By The Convergent Evolution Of Toxin Insensitivity.
Host: Jasmine Nirody
Despite the importance of adaptation in evolutionary biology, we still know little about how it occurs. What factors limit the rate of adaptation? To what extent is adaptation predictable? Emerging evidence suggests that a key factor constraining adaptations is “epistasis”, the effect of genetic background on the phenotypic and fitness properties of a mutation. Epistasis leads adaptation to be “path-dependent”, implying that evolutionary steps tend to occur in a particular, and ultimately predictable, order. Thus, it has become clear that to understand how novel protein functions evolve, we need to characterize how epistasis shapes adaptative paths, at the scales of genes and genomes. Motivated by these questions, we exploited the parallel evolution of cardiac glycoside (“CG”) resistance in the α-subunit of Na+,K+-ATPase (ATPα), across a diverse group of species. This system allowed us to computationally identify adaptive amino acid substitutions and validate them using in vivo engineering of the native (CG-sensitive) protein of Drosophila melanogaster. We showed that, despite conferring CG-insensitivity to ATPα in vitro, these substitutions are highly detrimental to organismal fitness in vivo. This finding was all the more surprising given the broad distribution of these adaptive amino acid substitutions among CG-tolerant animals. We further showed that these detrimental effects can be partly ameliorated by a “permissive” substitution, underscoring the importance of genetic background in adaptive evolution. I will also present our latest work revealing similar principles, with a twist, in frogs that prey on CG-producing toads.
March 9, 2021: (4PM via Zoom) – Dustin R. Rubenstein, Columbia University
Causes and Consequences of Sociality: Bidirectional Links between Behavior and the Genome .
Host: Jasmine Nirody
Highlighting our ongoing work in birds, insects, and crustaceans, I will illustrate how taking an integrative approach to the study of animal behavior is providing novel insights into social evolution. First, I will discuss how environmental variation shapes patterns of DNA methylation, alterative reproductive tactics, and lifetime inclusive fitness in cooperatively breeding African starlings. Next, I will examine the role chromosomal inversions play in local adaptation and the evolution of cooperation in Asian burying beetles. Finally, I will explore the dynamic relationship between genome evolution and social evolution by presenting data on genome size, transposon accumulation, and social organization in snapping shrimps. Ultimately, I will argue that considering the bidirectional links and feedbacks between social behavior and genome architecture, particularly in the context of environmental change, can provide new ideas about the evolution of complex animal societies and of cooperation more generally.
March 16, 2021: (4PM via Zoom) – N. Luisa Hiller, Carnegie Mellon University
Coupling Translational Fidelity with Cell Wall Biosynthesis in the Pneumococcus.
Host: David Zeevi
When exposed to stresses bacteria activate the stringent response pathway, a stress response that re-configures bacterial metabolism to ensure survival. Our recent work has found that the stringent response can be activated by increases in misaminoacylated tRNA (mostly toxic seryl-tRNAAla) which accumulates at low pH. In addition, we found that MurM, a cell wall biosynthesis enzyme, displays a strong preference for amino acids from the misaminoacylated seryl-tRNAAla , compared to the correctly acylated seryl-tRNASer or alanyl-tRNAAla. Thus, when MurM removes serine from misaminoacylated seryl-tRNAAla it serves as a gatekeeper of the stringent response pathway. Further, accumulation of seryl-tRNAAla leads to errors in translation, such that ability of MurM to deacylate these molecules contributes to translation fidelity. In the absence of MurM, accumulation of mischarged tRNAs triggers the stringent response, premature entry into stationary phase, and subsequent autolysis. In most domains of life, the pathological consequences of misaminoacylated tRNAs are mitigated by AlaXp, an enzyme that deacylates mischarged tRNAAla. Spn, and multiple other bacteria with thick cell walls, do not encode AlaXp. Our work suggests that MurM is an alternative evolutionary solution to the challenge of misaminoacetylation. Our findings implicate cell wall synthesis in the survival of bacteria as they encounter unpredictable and hostile conditions in the host. In this manner, environmental stresses may be reflected as variations in cell wall composition. The association between cell wall synthesis and translational fidelity is likely to be active in many other pathogens, given the distribution of MurM homologues and conservation in cell-wall cross-bridges.
March 30, 2021: (4PM via Zoom) – Elizabeth Wilbanks, University of California, Santa Barbara
Stuck Together: The Boons And Perils Of Multicellularity For Bacterial Consortia.
Host: David Zeevi
For more than fifty years, microbial consortia affectionately known as the “pink berries” for their macroscopic size and round, pink appearance have been observed in the Sippewissett Salt Marsh near Woods Hole, Massachusetts. These consortia, due to their persistence and abundance, present an unusual opportunity to observe and characterize in detail, the ecological interactions and evolutionary trajectories of wild bacteria. These aggregates are a symbiotic association of a single species of phototrophic purple sulfur bacteria and a sulfate reducing bacterial species, which exchange nutrients to catalyze an internal sulfur cycle at the nanometer scale. Our recent metagenomic work, including novel applications of long read DNA sequencing, has allowed us to reconstruct the complete genomes for bacteria in the consortia and characterize their population level variability over their geographic range. The unusual genomes of these multicellular bacteria, which are exceptionally large and rich in mobile genetic elements, indicate that viral predation exerts a strong selective pressure on the bacterial members of the consortia. We observe a rich diversity of both well characterized and novel viral defense strategies, including restriction modification systems, CRISPR-Cas, and novel toxin-antitoxin systems. In particular, we discovered novel toxins and second messenger systems that we hypothesize play a role in growth arrest or programmed cell death, and which are specifically overrepresented in other multicellular bacteria. This work offers insights how the ecological opportunities and evolutionary pressures of bacteria in multicellular consortia differ from those of single cells.
April 6, 2021: (4PM via Zoom) – Benjamin L. de Bivort, Harvard University
Functional and Structural Loci of Individuality in the Drosophila Olfactory Circuit.
Host: Jasmine Nirody
Behavior varies even among genetically identical animals raised in the same environment. However, little is known about stochasticity gives rise to circuit or anatomical variations underpinning this individuality. Drosophila olfaction presents an ideal model to study the biological basis of behavioral individuality, because while the neural circuit underlying olfactory behavior is well-described and highly stereotyped, persistent idiosyncrasy in behavior, neural coding, and neural wiring have also been described. Projection neurons (PNs), which relay odor signals sensed by olfactory receptor neurons (ORNs) to deeper brain structures, exhibit variable calcium responses to identical odor stimuli across individuals, but how these idiosyncrasies relate to individual behavioral responses remains unknown. Here, using paired behavior and two-photon imaging measurements, we show that PN, but not ORN, calcium responses predict individual preferences in a two-odor choice assay. Furthermore, paired behavior and immunohistochemistry measurements reveal that variation in ORN presynaptic density also predicts two-odor preference, suggesting this site is a locus of individuality where microscale circuit variation gives rise to idiosyncrasy in behavior. A challenge of testing hypotheses related to the origins of variability is the lack of experimental tools to manipulate stochastic outcomes. We have developed a spiking model of the entire antennal lobe, built on recent fly connectomic data sets. This circuit model is an in silico sandbox for testing hypotheses about the origins of stochastic variation, and points to local neurons (predominantly inhibitory antennal lobe interneurons) as having an outsized effect on PN odor representations that in turn predict idiosyncratic behavior. We believe that these experiments and models constitute an integrative approach to characterizing how functional and structural idiosyncrasy contribute to variable behavior among individuals.
April 13, 2021: (4PM via Zoom) – Oana Carja, Carnegie Mellon University
On Spatial Geometries That Suppress Or Amplify Rates Of Evolution.
Host: David Zeevi
Through recent innovations in imaging techniques and the ability to process these images at scale we have unprecedented access to public datasets on molecular and cellular spatial patterns of organization. In fact, modern molecular research increasingly relies on high-resolution image data as a major source of innovation. In addition, novel microfluidics and organoid technologies allow us to start building biological scaffolds that control the spatial topology of a molecular or cellular collective. To make full use of these new spatially-complex data streams and to go beyond simple quantification of pattern towards understanding of function, I believe we must first systematically understand how the spatial structure of a system shapes its evolutionary dynamics. What characterizes spatial topologies that act to amplify the selective advantage of new variants in the population, versus structures that dampen the force of selection and slow down rates of evolution?
This question of how the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes connected by migration corridors, symmetrical structures that most often act as well-mixed populations. Other studies use graphs and networks as a mathematical proxy for spatial architecture. However, they usually assume very small, regular networks, with strong constraints on the strength of selection considered. These symmetrical structures fundamentally fail to capture the complex pattern of interaction and the variance in local selection pressure present in natural populations, as well as in emerging spatial cellular and molecular atlases. Studying more complex topologies becomes a much harder problem. In this talk I will discuss how we build network generation algorithms, evolutionary simulations and derive general analytic approximations for studying rates of evolution in populations with complex spatial structure. By tuning network parameters and properties independent of each other, we systematically span across any network family and show that both a network’s degree distribution, as well as its node mixing pattern shape the evolutionary dynamics of new mutations.
In addition to the purely theoretical interest of these questions, as one application, we use recent microscopy datasets and build the cellular spatial networks of the stem cell niches of the bone marrow. We find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size decrease the suppression strength of the tissue spatial structure, hinting at a potential diminishing spatial suppression in the bone marrow tissue as individuals age.
This question of how the spatial arrangement of a population shapes its evolutionary dynamics has been of long-standing interest in population genetics. Most previous studies assume a small number of demes connected by migration corridors, symmetrical structures that most often act as well-mixed populations. Other studies use graphs and networks as a mathematical proxy for spatial architecture. However, they usually assume very small, regular networks, with strong constraints on the strength of selection considered. These symmetrical structures fundamentally fail to capture the complex pattern of interaction and the variance in local selection pressure present in natural populations, as well as in emerging spatial cellular and molecular atlases. Studying more complex topologies becomes a much harder problem. In this talk I will discuss how we build network generation algorithms, evolutionary simulations and derive general analytic approximations for studying rates of evolution in populations with complex spatial structure. By tuning network parameters and properties independent of each other, we systematically span across any network family and show that both a network’s degree distribution, as well as its node mixing pattern shape the evolutionary dynamics of new mutations.
In addition to the purely theoretical interest of these questions, as one application, we use recent microscopy datasets and build the cellular spatial networks of the stem cell niches of the bone marrow. We find these networks to be strong suppressors of selection, delaying mutation accumulation in this tissue. We also find that decreases in stem cell population size decrease the suppression strength of the tissue spatial structure, hinting at a potential diminishing spatial suppression in the bone marrow tissue as individuals age.
April 20, 2021: (4PM via Zoom) – Srividya Iyer-Biswas, Purdue University
Non-Trivial Status Quo: Stochastic Homeostasis, Growth and Form.
Host: Archishman Raju
Over 150 years since Claude Bernard coined the evocative phrase, “milieu intérieur”, to draw attention to a deep mystery in the natural world, fundamental questions remain open about how the simplest living system, a bacterial cell, maintains homeostasis of its best studied attribute, cell size. In this talk I will address how life shapes time in a bacterial cell, and examine the interplay between homeostasis and adaptation in this context. First, I will first establish that stochastic intergenerational bacterial cell size and shape homeostasis are maintained under appropriate growth conditions, using our high-precision data on growth and division of individual C. Crescentus cells. Next, the emergent simplicities revealed by these data. Following this, a fitting-free theoretical framework, consistent with observed scaling laws and phenomenology. This naturally leads to a proposal for the underlying mechanistic model. Finally, the extension to time-varying growth conditions, and new emergent simplicities.
April 27, 2021: (4PM via Zoom) – Sharlene Santana, University of Washington
Why Are Bats So Diverse? Integrating Macroevolutionary And Ecomorphological Studies To Understand The Bat Radiation.
Host: Jasmine Nirody
The adaptation to new diets is considered a major evolutionary driver of anatomical, behavioral and species diversity in mammals, but few quantitative studies have tested the impact of dietary evolution on morphological and species diversification across whole mammalian Orders. Bats are an ideal system to investigate this topic because they are exceptionally diverse in terms of number of species, skull morphology, diet, and sensory modalities used to locate food. In this talk, I will present two major areas of research in my lab that have allowed us to understand the patterns and mechanisms of bat diversification: analyses of cranial macroevolution across the bat radiation, and the coevolution between fruit bats and their mutualistic plants. These studies will highlight how a combination of sensory and dietary functions shaped the evolution of bat skull diversity through the modification of intrinsic mechanisms and functional adaptation, as well as the importance of bat sensory biases as agents of evolutionary change on their food resources.
May 4, 2021: (4PM via Zoom) – Matthew Fuxjager, Brown University
The Physiology of Display Evolution.
Host: Jasmine Nirody
How do elaborate animal signals evolve? My research explores this process from a physiological standpoint, in which I consider how forces like sexual selection interact with neuro-motor systems that govern movement to drive the emergence of novel dance displays used for sexual communication. I study a wide range of species, but my seminar will focus on my research in manakin birds. I’ve shown that, in these species, selection for elaborate display behavior underlies the evolution of extraordinary performance abilities largely by modifying the birds’ endo-muscular apparatus.
May 11, 2021: (4PM via Zoom) – Mecky Pohlschroeder, University of Pennsylvania
Novel aspects of Archaeal Cell Surface Biology: from Cell Biology to Glycoproteomics.
Host: Jasmine Nirody
Bacteria and archaea have complex cell envelopes that have several important functions, such as maintaining cell stability and shape, facilitating motility, and mediating adherence to surfaces. Although archaea are ubiquitous, present in all habitats examined thus far, including the human microbiome, compared to the bacteria, little is yet known about this domain of life. By combining methods from several disciplines to analyze the model archaeon Haloferax volcanii, we have been able to identify uniquely archaeal mechanisms, in addition to others shared with bacteria, that regulate certain aspects of cell surface biology. These include previously unknown mechanisms, involving the glycosylation of specific surface proteins that regulate biofilm formation. Most recently, we spearheaded the Archaeal Proteome Project (ArcPP), which has significantly advanced the efficient use of archaeal proteomic data and aided in the identification of the largest archaeal glycoproteome described thus far. We also showed that different N-glycosylation pathways can modify the same glycosites under the same culture conditions, revealing that, in addition to the remarkable range of proteins it modifies, Haloferax volcanii N-glycosylation is surprisingly complex. Furthermore, we have extended the phenotypic characterization of N-glycosylation pathway mutants in H. volcanii yielding new insights into the roles played by N-glycosylation in archaeal cell biology. The extensive glycoproteomics database established by ArcPP will not only be critical for the quantitative proteomic analysis of biofilms at different stages, but will also be an invaluable resource for studies of nearly all aspects of H. volcanii cell biology. Moreover, ArcPP can serve as an indispensable blueprint for comprehensive prokaryotic proteomics.
September 14, 2021: (4PM) – Travis Gibson, Harvard University
Learning Microbial Dynamics At Scale.
Host: Liat Shenhav
Learning microbial dynamics at scale (i.e. at human gut level complexity) is a necessary but challenging hurdle if we wish to make further progress in our scientific understanding of the microbiome and ultimately develop novel therapies. Toward that end, we present a scalable computational model of microbiome dynamics coupled with new dense time-series data of gnotobiotic mice colonized with microbiomes from different human donors (healthy and Ulcerative Colitis [UC]). With our computational model we learn how different groups of bacteria (modules) interact with each other, how they respond to perturbations, the stability and topology of the dynamics, and we assess module `keystoneness’. By learning these modules, we are able to characterize the dynamics of hundreds (or potentially thousands) of bacteria, whereas previous methods have only analyzed the top ten or so most abundant bacteria, or a priori clustered microbes. Furthermore, by colonizing genetically identical germ-free mice, we can begin to understand the intrinsic differences of the background microbiota independent of the host disease.
Our computational model addresses the key challenges of learning the dynamics of large and complex ecosystems from time series data. The central novel contribution of our model, and what allows for its scalability, are what we term interaction modules. These modules are learned groups of bacteria that share similar interactions and respond similarly to perturbations. Our mouse models also include key technical components that aid in our ability to learn microbial dynamics. For instance, we introduce multiple rounds of perturbations in our studies, creating rich transient dynamics with more information than would otherwise be available from multiple samples of steady state abundances.
Our time series mouse study was carried out over a period of 65 days with 77 longitudinal fecal samples collected per mouse (n=5 UC cohort and n=4 healthy cohort). Amplicon sequencing of the 16S rRNA gene to assess relative abundances and qPCR with universal 16S rRNA primers to quantitate bacterial concentration were performed on all samples. The number of inferred interaction modules for the healthy and UC cohorts is 11 and 16 respectively, with the interaction topology for the UC cohort being denser (40% connectivity vs 22%). Stability analysis showed that the UC cohort dynamics are four times more likely to be unstable compared to the healthy cohort. Ecological analysis revealed keystone modules that support cross-feeding in the healthy cohort while the UC cohort is dominated by competitive interactions leading to its instability. The module in the healthy cohort identified as the most important for promoting the growth of other modules contained four taxa all capable of starch degradation including Rominoccocus bromii which can degrade resistant starches RS(3), consistent with previous findings in the literature. We hope that these computational and experimental models will be broadly adopted as tools for disentangling the intrinsic dynamics of microbial populations from the host disease process, and whose predictions can help prioritize resources for more mechanistic studies.
Our computational model addresses the key challenges of learning the dynamics of large and complex ecosystems from time series data. The central novel contribution of our model, and what allows for its scalability, are what we term interaction modules. These modules are learned groups of bacteria that share similar interactions and respond similarly to perturbations. Our mouse models also include key technical components that aid in our ability to learn microbial dynamics. For instance, we introduce multiple rounds of perturbations in our studies, creating rich transient dynamics with more information than would otherwise be available from multiple samples of steady state abundances.
Our time series mouse study was carried out over a period of 65 days with 77 longitudinal fecal samples collected per mouse (n=5 UC cohort and n=4 healthy cohort). Amplicon sequencing of the 16S rRNA gene to assess relative abundances and qPCR with universal 16S rRNA primers to quantitate bacterial concentration were performed on all samples. The number of inferred interaction modules for the healthy and UC cohorts is 11 and 16 respectively, with the interaction topology for the UC cohort being denser (40% connectivity vs 22%). Stability analysis showed that the UC cohort dynamics are four times more likely to be unstable compared to the healthy cohort. Ecological analysis revealed keystone modules that support cross-feeding in the healthy cohort while the UC cohort is dominated by competitive interactions leading to its instability. The module in the healthy cohort identified as the most important for promoting the growth of other modules contained four taxa all capable of starch degradation including Rominoccocus bromii which can degrade resistant starches RS(3), consistent with previous findings in the literature. We hope that these computational and experimental models will be broadly adopted as tools for disentangling the intrinsic dynamics of microbial populations from the host disease process, and whose predictions can help prioritize resources for more mechanistic studies.
September 28, 2021: (4PM) – Sriram Sankararaman, University of California, Los Angeles
The Structure And Function Of Archaic DNA In Present-Day Humans.
Host: Liat Shenhav
Over the past decade, the ability to sequence genomes from both present-day and archaic humans (including our closest evolutionary relatives, the Neanderthals) has transformed our understanding of human history. Analyzing these genome sequences paints a picture of human history in which present-day humans migrated out of Africa but exchanged genes with multiple archaic human populations. I will describe statistical methods that identify segments of DNA inherited from archaic humans that are surviving in our genomes today and how these maps of introgressed archaic DNA are providing insights into human migration and biology. Despite this progress, our understanding of the contribution of archaic introgression to populations in Africa remains limited, in part due to the
challenges in obtaining ancient DNA in Africa. Leveraging recently developed statistical methods that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit 2-19% of their DNA from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor.
challenges in obtaining ancient DNA in Africa. Leveraging recently developed statistical methods that enable inferences about archaic populations without access to their genome sequences, we show that west African populations today inherit 2-19% of their DNA from an as-yet-unidentified archaic ghost population that diverged prior to the split of modern humans and Neanderthals. I will discuss the implications of these results for our understanding of human evolution as well as the statistical challenges that need to be solved in this endeavor.
October 5, 2021: (4PM) – Kristin M. Myers, Columbia University
Computational Biomechanical Models of Human Pregnancy – Evaluating the Risk of Preterm Birth.
Host: Liat Shenhav
The reproductive soft tissues that support the fetus undergo some of the most dramatic and unique growth and remodeling events in the human body. During pregnancy, the uterus and fetal membrane must grow and stretch to accommodate the fetus. Simultaneously, the cervix must remodel and be a mechanical barrier to keep the fetus within the uterus. All three tissues must withstand mechanical forces to protect, support, and maintain an optimal growth environment for the developing baby. Then, in a reversal of roles, ideally nearing term, the uterus begins to contract and the cervix deforms to allow for a safe delivery. The magnitude of stress and stretch of these soft tissues supporting the fetus are thought to control physiologic processes that regulate tissue growth, remodeling, contractility, and rupture, and it is generally hypothesized that these mechanical signals are clinical cues for normal labor and preterm birth, a major long-lasting public health problem with heavy emotional and financial consequences. In this talk I will reveal what we know about the soft tissue mechanics of pregnancy. I will present finite element models of pregnancy based on ultrasonic anatomical data, and I will examine the mechanical function of the soft tissues that support the fetus. I will also specifically characterize cervical material properties using a hyperelastic constitutive model that accounts for the cervical collagen fiber architecture and hormone-mediated remodeling relationships. Through this experimental and modeling effort I aim to identify which factor or combination of factors is responsible for clinically-observed mechanical dysfunction in pregnancy.
October 12, 2021: (4PM) – Madhav Mani, Northwestern University
The Genetics of Geometry and the Geometry of Genetics.
Host: Eric Siggia
My talk will focus on two current projects ongoing in my group with collaborating experimental labs. First, based on our development of a model of 3D cellular aggregates, we formulate an inverse mechanics problem that charts a course to the first mechanical atlas for embryogenesis at single-cell resolution over time. We demonstrate the accuracy and utility of such measurements within the context of light-sheet based live-imaging of Ascidian gastrulation. The longer term goal is to statistical juxtapose the RNA-based Cell Atlas to our Atlas of mechanical stresses, towards a more integrated view of the origins of morphology – The Genetics of Geometry. Second, motivated by a longstanding conflict between the manifest robustness of the developmental process and the requirement for variation to sustain its evolvability, we propose a new mathematical and statistical approach to study variation in adult forms. Focusing on a large ensemble of images of the fruit fly wing, in perturbed genetic and environmental conditions, we demonstrate that the set of possible varieties admissible by a core developmental program are highly constrained. Studying the species closely related to melanogaster, we demonstrate an alignment between the observed developmental and evolutionary variation.
October 19, 2021: (4PM) – Inigo Urteaga, Columbia University
Statistical Learning Of Menstruation From Indirect, Noisy And Missing Observations.
Host: Liat Shenhav
The menstrual cycle is a powerful indicator of overall health in women. However, its complete characterization remains an open research question, in part due to a lack of direct and reliable measurements of menstruation over-time and across individuals. In this talk, I will describe how statistical learning techniques can provide a better understanding of the menstrual cycle and its patterns, by accommodating different types of (noisy and missing not at random) observations: reproductive hormone level measurements through time and self-reported cycle start dates from menstrual trackers. The former are invasive and expensive to attain, but provide a detailed, in-depth view; the latter are easy and cheap to attain, but shallow and subject to biases. I will present statistical learning solutions that accommodate these noisy physiologic and self-tracked variables, towards robust, personalized models that can characterize menstruation.
October 26, 2021: (4PM via Zoom Only) – Tao Gao, University of California, Los Angeles
Modeling Theory of Mind for Competition, Cooperation and Communication.
Host: Liat Shenhav
Theory of mind (ToM) refers to the attribution of an agent’s motion to its mental states, including belief, desire and intention. Modeling ToM is built upon two principles. First, the “rationality principle” (utility theory), assuming that an agent takes actions to maximize its utility. Second, the Bayes’ theorem, solving ToM by maximizing the posterior of mental states conditioning on the observed actions. A model of ToM is a model of social commonsense that can explain a wide range of human interactions. I will start from a zero-sum chasing game, in which a human-controlled prey detects and avoids a computer-controlled predator. Both human and modeling results show that perceived chasing is severely disrupted when the predator’s actions violate the rationality principle, enabling the predator to stalk the prey stealthily. ToM becomes more prominent in cooperative tasks. Multi-agent ToM is challenging due to its recursive nature: I infer your inference of my inference of you. Here I advocate an “Imagined We”(IW) approach that avoids this recursion trap. “We” is defined as a super-agent that can rationality and centrally control all agents as body parts to maximize the joint utility. However, this “We” agent does not exist in reality. Instead, each agent actively imagines what We believes, and follows “what We wants me to do” voluntarily. IW predicts spontaneous “role assignment” and “goal commitment”. Furthermore, IW also serves as a cognitive infrastructure of human communication. I will show that the combination of cooperative logic, utility theory, and Bayes’ theorem strongly constrain the interpretation of even highly ambiguous signals, enabling humans to communicate so much by expressing so little.
November 2, 2021: (4PM) – Guillermo A. Cecci, IBM
Language At The Crossroads Of Mental Health And Artificial Intelligence.
Host: Marcelo Magnasco
Communication and language are fundamental building blocks of human behavior, and as such sit at the core of our understanding of mental disorders. We will describe work in recent years aimed at complementing and multiplying the ability of clinicians and researchers to diagnose, predict and monitor outcomes, and treat a wide spectrum of psychiatric and neurologic disorders, including schizophrenia spectrum disorders, neurodegeneration and drug intoxication, using tools from information technology and natural language processing. We will also discuss how, by learning from almost a century of neuropsychiatry practice and research, we hope to develop artificial intelligence models that are closer to the actual functioning of the human mind.
November 9, 2021: (4PM) – Gordon Berman, Emory University
Measuring The Hidden Dynamics Of Animal Behavior.
Host: Marcelo Magnasco
When we think of animal behavior, what typically comes to mind are actions – running, eating, swimming, grooming, flying, singing, resting. Behavior, however, is more than the catalogue of motions that an organism can perform. Animals organize their repertoire of actions into sequences and patterns whose underlying dynamics last much longer than any particular behavior. How an organism modulates these dynamics affects its success at accessing food, reproducing, and myriad other tasks essential for survival. Animals regulate these patterns of behavior via many interacting internal states (hunger, reproductive cycle, age, etc.) that we cannot directly measure. Studying these hidden states’ dynamics, accordingly, has proven challenging due to a lack of measurement techniques and theoretical understanding. In this talk, I will outline our efforts to uncover the latent dynamics that underlie long timescale structure in animal behavior. Looking across a variety of organisms, we use a novel methodology to measure animals’ full behavioral repertoires to find the existence of a non-trivial form of long timescale dynamics that cannot be explained using standard mathematical frameworks. I will present how temporal coarse-graining can be used to understand how these dynamics are generated and how the found course-grained states can be related to the internal states governing behavior through a combination of machine learning techniques and dynamical systems modeling. Inferring these hidden dynamics presents a new opportunity to generate insights into the neural and physiological mechanisms that animals use to select actions and live in the world.
November 30, 2021: (4PM) – Robert B. Phillips, Caltech
Deciphering the Genomic Rosetta Stone.
Host: Eric Siggia
As of this morning, the NIH Databases report roughly 6 x 10^16 nucleotides of genomic information, the equivalent of more than 60 billion copies of the complete works of Shakespeare, a thousand-fold larger than the book holdings of the Library of Congress. But how well do we really understand the meaning of those nucleotides? Even in arguably one of biology’s best understood organisms, the humble bacterium E. coli, for more than 60% of its genes we have no idea if or how they are regulated. The situation is even more grim with other celebrated model organisms, never mind something exotic such as a deep sea angler fish or an orca. In this talk, I will describe a strategy to overcome this regulatory ignorance. Specifically, I will show how using a combination of systematic mutagenesis, RNA-Seq and mass spectrometry, we can go from complete regulatory ignorance of some promoter of interest to a knowledge of both binding sites and the identity of the transcription factors that bind them. But then what? The second part of my talk will focus on how using the tools of statistical physics we can predict the input-output properties of these newly discovered regulatory architectures. In the spirit of little steps for little feet, I will show a thorough quantitative dissection of the most abundant regulatory motif in E. coli, the simple repression architecture.
December 7, 2021: (4PM) – Jasmine Nirody, The Rockefeller University
A Tale Of Two Motilities: Adaptive Biomechanics Across Scales.
Host: Liat Shenhav
Natural environments are heterogeneous and can fluctuate with time. As such, biomechanical systems from proteins to whole organisms have developed strategies to sense and deal with considerable spatial and temporal variability. I will discuss two (quite different!) broadly successful locomotive modes: flagellated motility in bacteria and walking in panarthropods. (1) A bacterium’s life can be complicated: it must swim through fluids of varying viscosity as well as interact with surfaces and other bacteria. We characterize the mechanosensitive adaptation in bacterial flagella that facilitates these transitions by using magnetic tweezers to manipulate external torque on the bacterial flagellar motor. Our model for the dynamics of load-dependent assembly in the flagellar motor illustrates how this nanomachine allows bacteria to adapt to changes in their surroundings. (2) Panarthropods are a diverse clade containing insects, crustaceans, myriapods and tardigrades. We show that inter-limb coordination patterns in freely-behaving tardigrades replicate several key features of walking in insects across a range of speeds and substrates. In light of these functional similarities, we propose a simple universal locomotor circuit capable of robust multi-legged control across body sizes, skeletal structures, and habitats.
December 14, 2021: (4PM via Zoom Only) – Eva-Maria S. Collins, Swarthmore University
The Quest For Immortality: Lessons From Planarians.
Host: Marcelo Magnasco
Over the last decade, my group has studied the reproduction dynamics of asexual planarians. Famous for their regenerative properties, these soft bodied, few mm long aquatic flatworms reproduce by ripping themselves into a head and tail piece using substrate traction and muscular forces. How they achieve this feat already fascinated Michael Faraday. However, because planarian self-bisection is experimentally challenging to study – self-bisection occurs infrequently (~ once/month), takes only a few minutes to complete, and stops at the slightest disturbance – this intriguing biomechanics’ problem remained a mystery. Using video recording at unprecedented spatial and temporal resolution, we observed the details of how individual planarians of three species self-bisect. Across species, the tensile stress needed for rupture is created through elongation and amplified using local constriction. How a planarian species divides dictates how resources are split among its offspring, determining offspring survival and reproductive success. Because self-bisection is the sole mode of reproduction in asexual planarians, this system also poses interesting questions about how genetic diversity arises in seemingly clonal populations. In my talk, I will show how we can explain certain aspects of planarian population strategies from the biomechanics of reproduction of individual worms. Furthermore, I will discuss how genetic diversity may arise in these clonal worms, based on ongoing work using long-term tracking of individuals and single planarian genomic sequencing. Finally, we will explore the question whether asexual planarians can defeat mortality by undergoing infinite division – regeneration cycles.
January 14, 2020 [Note Special Time: 2PM]: Harry McNamara, Harvard University
Synthetic Electrophysiology: Pattern formation and phase transitions in bioelectric tissues.
Host: E. Siggia
Electrical signaling in biology is typically associated with action potentials, transient spikes in membrane voltage that return to baseline. More generally, electrical tissues are reaction-diffusion systems which could support patterns which are structured in space but stationary in time. It is possible that electrical signaling could coordinate biological processes on timescales which are slower than action potentials – for example, during embryonic development. Constraints on electrophysiological measurement in vivo have made it challenging to assess these hypotheses quantitatively.
Here we present a new strategy to study biological pattern formation by building synthetic bioelectrical tissues from the bottom-up. We engineer electrically inert mammalian cells to express ion channels, optogenetic actuators, and fluorescent voltage indicators in tandem. By combining patterned illumination and all-optical electrophysiology, we study these synthetic bioelectric tissues as excitable media and compare their dynamics rigorously to mathematical predictions.
In one demonstration, we engineer bioelectrical circuits of spiking cells capable of simple information processing and memory(1) We also show that dynamical transitions between stable and arrhythmic spiking patterns in these tissues depends sensitively on tissue geometry and dimensionality(2). Finally, we show that synthetic tissues of electrically bistable cells can form spatially structured but time-stationary electrical domains which polarize via nucleation-and-growth phase transitions(3). Observation of electrical domains in a stem cell model of myogenesis suggest that bioelectrical phase transitions may play a physiological role during embryonic development.
Here we present a new strategy to study biological pattern formation by building synthetic bioelectrical tissues from the bottom-up. We engineer electrically inert mammalian cells to express ion channels, optogenetic actuators, and fluorescent voltage indicators in tandem. By combining patterned illumination and all-optical electrophysiology, we study these synthetic bioelectric tissues as excitable media and compare their dynamics rigorously to mathematical predictions.
In one demonstration, we engineer bioelectrical circuits of spiking cells capable of simple information processing and memory(1) We also show that dynamical transitions between stable and arrhythmic spiking patterns in these tissues depends sensitively on tissue geometry and dimensionality(2). Finally, we show that synthetic tissues of electrically bistable cells can form spatially structured but time-stationary electrical domains which polarize via nucleation-and-growth phase transitions(3). Observation of electrical domains in a stem cell model of myogenesis suggest that bioelectrical phase transitions may play a physiological role during embryonic development.
- McNamara, H.M., Zhang, H., Werley, C.A. and Cohen, A.E., 2016. Optically controlled oscillators in an engineered bioelectric tissue. Physical Review X, 6(3), p.031001.
- McNamara, H.M., Dodson, S., Huang, Y.L., Miller, E.W., Sandstede, B. and Cohen, A.E., 2018. Geometry-dependent arrhythmias in electrically excitable tissues. Cell Systems, 7(4), pp.359-370.
- McNamara, H.M., Salegame, R., Al Tanoury, Z., Xu, H., Begum, S., Ortiz, G., Pourquie, O. and Cohen, A.E., 2019. Bioelectrical signaling via domain wall migration. bioRxiv, p.570440. (in press, Nature Physics).
January 16, 2020 [Note Special Time: 2PM]: Lishibanya Mohapatra, Brandeis University
How cells control the size of their organelles.
Host: E. Siggia
Cells contain a number of micron-scale structures, whose physiological functions are related to their size. Examples include cytoskeletal elements like mitotic spindle, cilia and actin cables. Each of these structures is characterized by a narrow size distribution and is composed of molecular building blocks (tubulin dimers and actin monomers) that diffuse in the cytoplasm. A key question is how the size of these structures is maintained in light of constant turnover of their molecular components. Using theory, simulations and experiments in various cell types, I will describe how we can aim to uncover design principles of size-control in biology.
January 21, 2020 [Note Special Time: 2PM]: Jennifer Crodelle, New York University
A model for the development of orientation preference maps in the visual cortex of mice.
Host: E. Siggia
The mammalian primary visual cortex (V1) contains neurons that respond preferentially to oriented visual stimuli (e.g., horizontal bars). In the mouse, these orientation-preferring neurons are scattered throughout V1 in what’s called a “salt and pepper” orientation-preference (OP) map. Despite the seemingly random distribution of OPs in the visual cortex of mice, it has been shown that radially-distributed clonally-related cells show similar stimulus feature selectivity, as well as preferential synaptic connectivity with fellow sister cells. Importantly, each of these characteristics relies on gap-junction coupling between sister cells during the first postnatal week. We construct an idealized model of the mouse visual cortex during the first two postnatal weeks of development and analyze the effect of gap-junction coupling on the formation of synaptic connections both into and within V1. In particular, we use this model to propose a role for gap-junction coupling between sister cells in facilitating the formation of the salt-and-pepper OP that is typical of the adult mouse visual cortex.
January 23, 2020 [Note Special Time: 2PM]: Liat Shenhav, University of California, Los Angeles
Spatiotemporal modeling of microbial communities.
Host: E. Siggia
Microbial communities can undergo rapid changes, that can both cause and indicate host disease, rendering longitudinal microbiome studies key for understanding microbiome-associated disorders. However, most standard statistical methods, based on random samples, are not applicable for addressing the methodological and statistical challenges associated with repeated, structured observations of a complex ecosystem. Therefore, to elucidate how and why our microbiome varies in time, and whether these trajectories are consistent across humans, we developed new methods for modeling the temporal and spatial dynamics of microbial communities.
We developed a method to identify ‘time-dependent’ microbes (Shenhav et al., PLoS Computational Biology 2019) and showed that their temporal patterns differentiate between the developing microbial communities of infants and those of adults. In a different project, we derived a new nonlinear system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), and addressed a longstanding challenge in the field by showing that relative abundance trajectories predicted by this new method are as accurate as trajectories predicted using the standard Lotka-Volterra model (Joseph, Shenhav et al., in review). We also developed models to deconvolute the dynamics of microbial community formation. Using these methods, we found significant differences between vaginally- and cesarean-delivered infants in terms of initial colonization and succession of their gut microbial community (Shenhav et al., Nature Methods 2019) as well as the trajectories of these communities in the first years of life (Martino*, Shenhav* et al., in prep.). These models, designed to identify and predict time-dependent patterns, would help us better understand the temporal nature of the human microbiome from the time of its formation at birth and throughout life.
We developed a method to identify ‘time-dependent’ microbes (Shenhav et al., PLoS Computational Biology 2019) and showed that their temporal patterns differentiate between the developing microbial communities of infants and those of adults. In a different project, we derived a new nonlinear system for microbial dynamics, termed “compositional” Lotka-Volterra (cLV), and addressed a longstanding challenge in the field by showing that relative abundance trajectories predicted by this new method are as accurate as trajectories predicted using the standard Lotka-Volterra model (Joseph, Shenhav et al., in review). We also developed models to deconvolute the dynamics of microbial community formation. Using these methods, we found significant differences between vaginally- and cesarean-delivered infants in terms of initial colonization and succession of their gut microbial community (Shenhav et al., Nature Methods 2019) as well as the trajectories of these communities in the first years of life (Martino*, Shenhav* et al., in prep.). These models, designed to identify and predict time-dependent patterns, would help us better understand the temporal nature of the human microbiome from the time of its formation at birth and throughout life.
February 4, 2020: Gabriel D. Victora, The Rockefeller University
Directing evolution: Can we choose what the immune system sees?
Host: E. Siggia
Not available
February 18, 2020: Adam Cohen, Harvard University
Optical electrophysiology for dissecting cortical microcircuits.
Host: E. Siggia
Combined optogenetic perturbation and voltage imaging enables high-resolution mapping of bioelectrical dynamics in intact tissues. I will describe application to Layer 1 of the mouse barrel cortex. We developed techniques to distinguish the separate contributions of excitatory and inhibitory synaptic inputs to membrane potential. Using these tools, we deciphered how the sensory-evoked responses emerged from local network dynamics.
February 25, 2020: Bojan Zagrovic, University of Vienna
RNA-protein interactions and the structure of the genetic code.
Host: A. Vaziri
The notion of physicochemical complementarity is one of the most powerful mechanistic paradigms in molecular biology. Recently, we have revealed a robust, statistically significant matching between the nucleobase-density profiles of mRNA coding sequences and the nucleobase-binding profiles of the protein sequences they encode. For example, purine-density profiles of mRNA sequences mirror the guanine-affinity profiles of their cognate protein sequences with quantitative accuracy (median Pearson correlation coefficient |R| = 0.80 across the entire human proteome). Overall, our results support as well as redefine the stereochemical hypothesis concerning the origin of the genetic code, the idea that the code evolved from direct interactions between amino acids and the appropriate bases. Moreover, our findings support the possibility of direct, complementary, co-aligned interactions between mRNAs and their cognate proteins even in present-day cells, especially if both are unstructured, with implications extending to different facets of nucleic-acid/protein biology. In this talk, I will focus on different lines of evidence regarding the complementarity hypothesis, with a particular focus on experimental UV-crosslinking and immunoprecipitation (CLIP) results.
March 3, 2020: Simon Tavare, Columbia University
Some statistical problems in cancer evolution.
Host: E. Siggia
This talk addresses some statistical and computational problems arising in the study of cancer evolution. The starting point comes from population genetics: how should we estimate evolutionarily relevant parameters from DNA sequence data taken from samples of individuals?
I will give a brief overview of what we learned, touching on Approximate Bayesian Computation as an inference method when likelihoods are intractable. To illustrate ABC I will give an example concerning inference about the number of distinct DNA sequences in a sample, given only information about the relative frequency of point mutations in the samples. This provides an introduction to inference from typical cancer sequencing data, in which individuals are replaced by cells and in which typically we do not know which mutations occur in which cells. I will discuss a stochastic model that exploits coalescent theory to study clonal sweeps, and describe new techniques for deconvolving clones from single cell sequencing data. Time permitting, I will describe some novel experimental methods we are developing to understand the 3D structure of tumors, paving the way for some challenging inferential problems that will require engagement from data scientists and others.
I will give a brief overview of what we learned, touching on Approximate Bayesian Computation as an inference method when likelihoods are intractable. To illustrate ABC I will give an example concerning inference about the number of distinct DNA sequences in a sample, given only information about the relative frequency of point mutations in the samples. This provides an introduction to inference from typical cancer sequencing data, in which individuals are replaced by cells and in which typically we do not know which mutations occur in which cells. I will discuss a stochastic model that exploits coalescent theory to study clonal sweeps, and describe new techniques for deconvolving clones from single cell sequencing data. Time permitting, I will describe some novel experimental methods we are developing to understand the 3D structure of tumors, paving the way for some challenging inferential problems that will require engagement from data scientists and others.
March 5, 2020: Konrad Kording, University of Pennsylvania
Tuning to many features.
Host: A. Vaziri
I will present a sequence of analyses of neural tuning properties where we ask how a set of dimensions affect tuning. In area V4, we analyzed tuning to natural and artificial stimuli asking how similar those tunings are. We find that they are hugely complex and are vastly different between artificial and natural conditions. In basal ganglia and M1 we tried to estimate the dimensionality of tuning and find that the dimensionality is very high. I will review theoretical insights of why such complex tuning may be a very efficient strategy for the brain.
March 10, 2020: Daniel Fletcher, University of California, Berkeley
Mind the gap: Size-based organization at cell-cell contacts.
Host: E. Siggia
Membrane interfaces formed at junctions between cells are often associated with characteristic patterns of protein organization, such as in epithelial tissues and between cells of the immune system. Size is emerging as a critical feature of cell surface proteins that can directly affect cell-cell interface formation and contribute to spatial arrangement of proteins at junctions, as well as their downstream signaling. This talk will present a new method for characterizing cell surface protein size that enables nanometer-scale height measurements, and I will describe the implications of protein height on macrophage phagocytosis, cell-cell fusion, and viral entry. Results from these studies support a model in which the topography of cell surfaces plays a key role in mediating cell-cell interaction and communication.
March 31, 2020: (via Zoom) – Alex Mogilner, New York University
Feedbacks between mechanics and geometry ensures almost deterministic mitotic spindle assembly.
Host: E. Siggia
One of the most fundamental cell biological events is assembly of the mitotic spindle. Two existent models of the spindle assembly are 1) search-and-capture (SAC) and 2) acentrosomal microtubule assembly (AMA). SAC model is pleasingly simple: microtubules (MTs), organized into two asters focused at two centrosomes, grow and shrink randomly. As soon as a growing MT end bumps into a kinetochore (KT), the connection between the spindle pole and chromosome is established. This model predicts that KTs are captured at random times and that slow spindle assembly is plagued by errors. For decades, the SAC model seemed to work. Our recent data ruins the SAC model and suggests that a hybrid between SAC and AMA models could work. I will explain how we used 3D tracking of centrosomes and KTs in animal cells to develop a computational model, which explains the remarkable speed and precision of the almost deterministic process of the spindle assembly emerging from random and imprecise molecular events.
April 7, 2020: (via Zoom) – Gunter Wagner, Yale University
How to turn a crisis into a new identity?
The origin of a novel cell type through signaling network restructuring.
The origin of a novel cell type through signaling network restructuring.
Host: E. Siggia
The number of traceable cell types varies massively among multicellular animals from somewhere between 5 and 30 in the anatomically most primitive animals (e.g. Trichoplax or sponges) to over 500 histologically distinguishable cell types in humans, the latter number likely an underestimate possibly by a factor of five or six. Hence, the evolutionary origin of novel cell types is a major mode of how body plan complexity evolves. While there is a good amount of research dedicated towards mapping the evolutionary history of cell type evolution and homology, driven by single cell technology, the mechanistic basis for the origin of novel cell types is entirely unknown. In my lab we are addressing this problem by examining the origin of a cell type, the decidual stromal cell (DSC), that only exists in placental [eutherian] mammals, and is thus “only” 100 to 65 Mio years old. The comparison of cell differentiation in an outgroup, the opossum, with that of human cells shows that a large part of the gene regulatory network underlying the differentiation of DSCs utilizes a network that in opossum regulates the cellular stress reaction, likely homologous to the fibroblast activation during wound healing. Fibroblast activation is a transient self-limiting process, and the origin of a novel cell type consists of modifications of the signaling network that allow the assumption of a sustainable gene regulatory state. In brief, the comparative data suggests that key steps in the origin of the DSC were 1) the permanent inhibition of Akt-pathway activity, releasing key transcription factors like FOXO1 from degradation, 2) a sustained activation of the PKA pathway by PGE2, 3) an alternative mechanism for inhibiting JNK dependent apoptosis, after Akt inhibition of JNK has been removed, and 4) the development of autocrine signals to replace the transient paracrine signals. On a general level these results suggest that a narrow gene regulatory network perspective is insufficient to understand the mechanisms of cell type origination.
April 14, 2020: (via Zoom) – Felicity Muth, University of Texas, Austin
New insights into the cognitive ecology of pollination.
Host: J. Nirody
Perception, learning and memory in animals are often studied under fairly artificial lab conditions. However, like any other trait, cognition is shaped by natural selection and the animals’ ecological environment, meaning simplifications can often be misleading. Bees are an insect model system for understanding cognition, yet the majority of what we know about this topic has been carried out under artificial lab conditions, using nectar rewards (in the form of sucrose solution). Bees also collect pollen, their main source of protein and critical for colony survival. Here I present a series of experiments investigating bee learning from an ecological perspective. I will also discuss recent work on how neonicotinoid pesticides affect bee behavior and cognition.
April 21, 2020: (via Zoom) – Jacopo Grilli, International Centre for Theoretical Physics, Trieste, IT
Laws of Diversity and Variation in Microbial Communities.
Host: J. Nirody
How the coexistence of many species is maintained is a fundamental and unanswered question in ecology. Coexistence is a puzzle because we lack a quantitative understanding of the variation in species presence and abundance. Whether variation in ecological communities is driven by deterministic or random processes is one of the most controversial issues in ecology. I will consider the variation of species presence and abundance in microbial communities from a macroecological standpoint. We identify three novel, fundamental, and universal macroecological laws that characterize the fluctuation of species abundance across communities and over time. These three laws — in addition to predicting the presence and absence of species, diversity and other commonly studied macroecological patterns — allow testing mechanistic models and general theories aiming at describing the fundamental processes shaping microbial community composition and dynamics. I will conclude by showing that a mathematical model based on environmental stochasticity quantitatively predicts the three macroecological laws, as well as non-stationary properties of community dynamics.
May 5, 2020: (via Zoom) – Martha Munoz, Yale University
Behavior is a Motor and a Brake for Evolution.
Host: J. Nirody
One of the most striking patterns of evolution is its uneven tempo across the tree of life. Whereas some traits and lineages diversify rapidly, others appear to remain inert over millions of years. But, why is this so? What allows some features to achieve evolutionary overdrive, and why do some traits appear to straddle evolution’s slow lane? I explore this question by focusing on one of evolution’s key architects: behavior. I illustrate how organisms are not the passive targets of selection; rather, through behavior, they can be the agents of selection. Using Caribbean Anolis lizards as a model system, I reveal the signatures of behavior at both micro- and macroevolutionary scales, and illustrate the constraints on this phenomenon. Behavior can slow or hasten evolution and, on occasion, it does both simultaneously.
May 12, 2020: (via Zoom) – Grant Rotskoff, Stanford University
Learning Transition States: Approximation, Sampling, and Optimization with Rare Data.
Host: J. Nirody
The surprising flexibility and undeniable empirical success of machine learning algorithms has inspired many theoretical explanations for the efficacy of neural networks. Here, I will briefly introduce one perspective that provides not only asymptotic guarantees of trainability and accuracy in high-dimensional learning problems, but also provides some prescriptions and design principles for learning. Bolstered by the favorable scaling of these algorithms in high dimensional problems, I will turn to a central problem in computational condensed matter physics—that of computing reaction pathways. From the perspective of an applied mathematician, these problems typically appear hopeless; they are not only high-dimensional, but also dominated by rare events. However, with neural networks in the toolkit, at least the dimensionality is somewhat less intimidating. I will describe an algorithm that combines stochastic gradient descent with importance sampling to optimize a function representation of a reaction pathway for an arbitrary system. Finally, I will provide numerical evidence of the power and limitations of this approach.
May 19, 2020: (via Zoom) – Eleni Katifori, University of Pennsylvania
Design Principles for Networked Flow Systems.
Host: J. Nirody
Complex life larger than a humble nematode would not be possible without a circulatory system. Plants, fungi, and animals have developed vascular systems of striking complexity to solve problems of long distance nutrient delivery, waste removal, and information exchange. These disparate vascular systems are constrained by the same physics and their structure is governed by the common universal principles such as a hierarchy in the vessel diameters and the existence of multiple loops. Typically, biological transport networks have to satisfy competing demands to operate efficiently and robustly while confronted with an ever-changing environment. In this talk we present several examples that highlight how topology is coupled to function in flow networks. We model vascular webs using electric circuit analogues with complex architectures. From delivering oxygen and nutrients homogeneously in capillary beds, to information transmission and the hydrodynamic coupling between the elastic vessel wall and the blood in pulsatile flow networks, we explore how circulatory systems have adapted to optimally perform a varied array of tasks.
September 15, 2020: (4PM via Zoom) – Sarah Veatch, University of Michigan
The Miscibility Phase Transition in Membranes and its Impacts on Cellular Functions.
Host: Archishman Raju
Researchers have long been interested in the roles that lipids can play in clustering proteins on the surface of cells, bolstered by biochemical isolations and observations of phase separated liquid domains in model membrane vesicles of purified lipids or isolated from cells. Experimental evidence from intact cells acquired over decades suggests that lipid-mediated protein clustering is subtle, if present at all. Nonetheless, it is clear that factors that influence the miscibility transition in membranes also impact a broad range of cell functions, and that cells biologically tune their membrane composition in ways that impacts this phase transition. This talk will begin to reconcile this apparent contradiction through experimental investigations of initial B cell receptor activation and the action of n-alcohol general anesthetics, two systems where we argue the membrane phase transition contributes to function. This talk will also discuss some important differences between membrane domains and phase separated liquid droplets observed in the cytoplasm and nucleus, suggesting different functional roles for these two classes of miscibility transitions in cells.
September 22, 2020: (4PM via Zoom) – Sujit Datta, Princeton University
Life in a Tight Spot: Bacterial Motility in Heterogeneous Media.
Host: Jasmine Nirody
Diverse processes in healthcare, agriculture, and the environment rely on bacterial motility in porous media; indeed, most bacterial habitats—e.g. biological gels, tissues, soils, and sediments—are heterogeneous porous media. However, while bacterial motility is well-studied in homogeneous environments, how confinement in a porous environment impacts bacterial transport remains poorly understood. To address this gap in knowledge, we combine microscopy, materials fabrication, and microbiology to investigate how E. coli moves in 3D porous media. By probing single cells, we demonstrate that the paradigm of run-and-tumble motility is dramatically altered by pore-scale confinement. Instead, we find a new mode of motility in which cells are intermittently and transiently trapped as they navigate the pore space; analysis of these dynamics enables prediction of bacterial transport over large length and time scales. Further, by developing a new 3D printing approach, we design multi-cellular communities with precise control over the spatial distribution of bacteria. Using this approach, we show that concentrated populations can collectively migrate through a porous medium—despite being strongly confined—and develop principles to predict and direct this behavior.
September 29, 2020: (4PM via Zoom) – Vijay Balasubramanian, University of Pennsylvania
The Size of the Immune Repertoire of Bacteria
Host: Archishman Raju
Some bacteria and archaea possess an adaptive immune system that maintains a memory of past infections in viral DNA elements called spacers stored in the CRISPR loci of their genomes. This memory is used to mount targeted responses against later threats, but is remarkably shallow: it remembers only a few dozen to a few hundred viruses. I will present a statistical theory of CRISPR-based immunity that quantitatively explains the depth of bacterial immune memory in terms of a trade-off with fundamental constraints of the cellular biochemical machinery. Given known cross-reactive mechanisms of CRISPR interference and primed spacer acquisition, the theory further suggests that the incorporation of phage DNA also creates a significant threat of auto-immunity. I will show that balancing viral defense against auto-immunity predicts a scaling law that relates spacer length and CRISPR repertoire size. Analysis of a publicly available database of microbial genomes shows that this scaling law is realized empirically across prokaryotes, partly through proportionate use of different CRISPR-Cas types in strains carrying multiple loci. Finally, I will demonstrate population-level selection mechanisms that can generate the observed scaling law.
October 6, 2020: (4PM via Zoom) – Mark Transtrum, Brigham Young University
Using Information Geometry to Find Simple Models of Complex Processes.
Host: Archishman Raju
Effective theories play a fundamental role in how we reason about the world. Although real physical processes are very complicated, useful models abstract away the irrelevant degrees of freedom to give parsimonious representations. I use information geometry to construct simplified models for many types of complex systems, such as biology, neuroscience, statistical physics, and complex engineered systems. I interpret a multi-parameter model as a manifold embedded in the space of all possible data, with a metric induced by statistical distance. These manifolds are often bounded and very thin, so they are well-approximated by a low-dimensional, simple model. For many types of models, there is a hierarchy of natural approximations that reside on the manifold’s boundary. These approximations are not black-boxes. They remain expressed in terms of the relevant combinations of mechanistic parameters and reflect the physical principles on which the complicated model was built. They can also be constructed in a systematic way using computational differential geometry. Finally, I discuss the topological relationship among reduced models and how it informs questions such as large-scale reduction and model transferability..
October 13, 2020: (4PM via Zoom) – Paulo Arratia, University of Pennsylvania
Life in Complex Fluids.
Host: Jasmine Nirody
Many microorganisms (e.g. bacteria, algae, sperm cells) move in fluids or liquids that contain (bio)-polymers and/or solids. Examples include human cervical mucus, intestinal fluid, wet soil, and tissues. These so-called complex fluids often exhibit nonlinear rheological behavior due to the non-trivial interaction between the fluid microstructure and the applied stresses. In this talk, I will show how the presence of polymers in the fluid medium can strongly affect the motility (i.e. swimming) behavior of microorganisms such as C. elegans, C. reinharditti, and E. coli. Our results show how fluid elasticity can hinder the swimming speed of the undulatory swimmer C. elegans. The presence of boundaries, however, significantly affects this trend and shows non-monotonic behavior. Next, I will discuss how fluid elasticity affect the swimming behavior of the bacterium E. coli and the algae C. reinhardtii, a pusher and puller swimmer respectively. We find that fluid elastic stresses can significantly affect the run-and-tumble mechanism characteristic of E. coli, as well as flagellar waveforms of C. reinharditii. These results demonstrate the intimate link between swimming kinematics and fluid rheology and that one can control the spreading and motility of microorganisms by tuning fluid properties.
October 20, 2020: (4PM via Zoom) – Richard Neher, University of Basel
Tracking and Predicting the Evolution of Human RNA Viruses.
Host: Eric Siggia
Many RNA viruses change their antigenic properties rapidly and reinfect humans repeatedly throughout their lives, resulting in a coupled dynamics of the virus population and the collective antibody repertoire of the human population. Starting from simple models of evolution, we developed methods to infer fitness of co-circulating viruses and predict which variants are likely to be successful. These methods are now used to inform the twice-yearly influenza vaccine composition recommendations by the WHO. In a parallel effort, we have developed an interactive online platform for real-time phylogenetic analysis and tracking of viruses called Nextstrain. Nextstrain was initially designed to track influenza viruses and was gradually extended to several other viruses, including SARS-CoV-2. I will discuss how we track global spread and evolution of SARS-CoV-2 using Nextstrain.
October 27, 2020: (4PM via Zoom) – Mary (Cassie) Stoddard, Princeton University
Diversity of Form and Function in the Avian World: Lessons from Tiny Hummingbirds, Giant Emus and Other Birds.
Host: Jasmine Nirody
Birds evolved about 150 million years ago, and today they are the most diverse and colorful land vertebrates. In my group, we are fascinated by the ecological and evolutionary processes that drive this variation. Much of our work investigates coloration and vision in birds. A fundamental challenge is that birds see differently from humans: they have tetrachromatic vision (four color cone-types) and ultraviolet sensitivity. To estimate a “bird’s-eye view,” we combine advanced imaging techniques with new computational methods. This has allowed us to test ideas about how birds use color to attract mates, avoid predators and deceive rivals. In the field, we are establishing a system for studying color perception in wild hummingbirds in the Rocky Mountains. These tiny iridescent birds lead colorful lives, performing spectacular courtship dives and pollinating diverse wildflowers. We also study the avian egg, a remarkable structure that is tough but breakable. The eggs laid by stealthy cuckoos and flightless emus offer insights into avian behavior and evolution. We apply a highly interdisciplinary approach, combining tools from mathematics, computer vision and bioengineering, to explore the avian world.
November 3, 2020: (4PM via Zoom) – David Sivak, Simon Fraser University
Revving Your Molecular Machine: Nonequilibrium Driving and Internal Coupling.
Host: Jasmine Nirody
Biomolecular machines are central actors in a myriad of major cell biological processes. Their successful function requires effective energy conversion among diverse mechanical components, and time-reversal symmetry-breaking to achieve directed transport. It seems plausible that evolution has sculpted these machines to effectively transduce free energy in their natural contexts, where stochastic fluctuations are large, nonequilibrium driving forces are strong, and biological imperatives require rapid turnover. But what are the physical limits on such nonequilibrium performance, and what machine designs actually achieve these limits? In this talk, I discuss how to maximize a molecular machine’s power by (1) allocating nonequilibrium driving forces nonuniformly among the steps of the machine cycle and (2) moderating the connection strength between its internal components. These results provide nontrivial yet intuitive implications for the design principles of molecular-scale free energy transduction.
November 10, 2020: (4PM via Zoom) – Jianhua Xing, University of Pittsburgh
Reconstructing Cell Phenotypic Transition Dynamics from Single Cell Data.
Host: Jasmine Nirody
Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, i.e., cell phenotypic transitions (CPTs). Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging. My lab is tackling these grand challenges from two directions, with the ultimate goal of integrating the two directions to reconstruct the spatial-temporal dynamics of CPTs.
In one direction, we developed a live-cell imaging platform that tracks cellular status change in a composite multi-dimensional cell feature space that include cell morphological and texture features readily through fluorescent and transmission light imaging1. We also introduced transition path analyses and the concept of reaction coordinate from the well-established rate theories into CPT studies2. We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT).
In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data3. The work is inspired by recent work of estimating RNA velocities (instant time derivatives (dx/dt, with x is the cell expression state)4. We generalized the procedure for more accurate estimation of RNA velocities from scRNA-seq data with metabolic labeling (and other types of single cell data). Then formulating it as a machine learning problem, we developed a procedure of learning the analytical form of the vector field F(x) and the equation dx/dt = F(x) in the Reproducing Kernel Hilbert Space. Further differential geometry analysis on the vector field reveals rich information on gene regulations and dynamics of various CPT processes.
In one direction, we developed a live-cell imaging platform that tracks cellular status change in a composite multi-dimensional cell feature space that include cell morphological and texture features readily through fluorescent and transmission light imaging1. We also introduced transition path analyses and the concept of reaction coordinate from the well-established rate theories into CPT studies2. We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT).
In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data3. The work is inspired by recent work of estimating RNA velocities (instant time derivatives (dx/dt, with x is the cell expression state)4. We generalized the procedure for more accurate estimation of RNA velocities from scRNA-seq data with metabolic labeling (and other types of single cell data). Then formulating it as a machine learning problem, we developed a procedure of learning the analytical form of the vector field F(x) and the equation dx/dt = F(x) in the Reproducing Kernel Hilbert Space. Further differential geometry analysis on the vector field reveals rich information on gene regulations and dynamics of various CPT processes.
November 17, 2020: (4PM via Zoom) – Timothy Linksvayer, University of Pennsylvania
Genomic Signatures of the Convergent Evolution of Sociality in Spiders and Insects.
Host: Jasmine Nirody
Sociality is a conspicuous phenotypic innovation that convergently evolved repeatedly and sporadically across vertebrates, insects, spiders, and crustaceans. A series of transcriptomic and comparative genomic studies in social insects has begun to identify putative genomic signatures of the evolution of complex societies, including expansion of certain gene families and signatures of elevated molecular evolution. I will discuss ongoing research in my lab, focused on comparative genomic and transcriptomic approaches seeking to identify genomic signatures of the convergent evolution of sociality in spiders and social insects. This includes a new comparative study of 25 spider transcriptomes, representing seven independent origins of sociality. I will also briefly discuss ongoing research in my lab focused on elucidating the genetic basis and evolution of social traits using the pharaoh ant Monomorium pharaonis.
December 1, 2020: (4PM via Zoom) – Edo Kussell, New York University
Memory in Bacteria: From Single Cells to Ecologies.
Host: Archishman Raju
Bacteria have a very large toolbox of molecular mechanisms, coded by specific genes, which enable survival under stressful conditions. But these mechanisms on their own are often inefficient or costly, and only by using them strategically do bacteria gain a long-term advantage. This talk examines the strategies that bacteria use to regulate their survival toolbox.
By encoding and using memory in different ways, bacteria can optimize their long-term growth potential. This optimization can be understood by a statistical mechanics analogy. I describe a phase diagram structure in which memory levels are optimized as a function of the statistics of a randomly fluctuating environment, and a bacterial survival strategy can undergo different types of phase transitions.
I will illustrate these ideas using two groundbreaking experiments in microbiology, one which led to the discovery of gene regulation by Jacques Monod in the 1940’s, and the second on co-evolution of bacteria and phages by Meyer, Lenski and co-workers in the 2010’s. In both cases, bacteria encode memory of previous stresses and use it to their advantage.
By encoding and using memory in different ways, bacteria can optimize their long-term growth potential. This optimization can be understood by a statistical mechanics analogy. I describe a phase diagram structure in which memory levels are optimized as a function of the statistics of a randomly fluctuating environment, and a bacterial survival strategy can undergo different types of phase transitions.
I will illustrate these ideas using two groundbreaking experiments in microbiology, one which led to the discovery of gene regulation by Jacques Monod in the 1940’s, and the second on co-evolution of bacteria and phages by Meyer, Lenski and co-workers in the 2010’s. In both cases, bacteria encode memory of previous stresses and use it to their advantage.
December 8, 2020: (4PM via Zoom) – Eugene Koonin, National Center for Biotechnology Information, National Institutes of Health
Evolution of Complexity at the Interface of Conflicts and Cooperation.
Host: Archishman Raju
What are the origin and routes of evolution of the enormous hierarchical complexity of life that has no precedence in inanimate matter? Arguably, this is the ultimate question of evolutionary biology if not of biology as a whole. I submit that a major aspect of the answer is that complexity evolution is driven by various types of biological conflicts, or in more physical terms, competing interactions and frustrated states that are apparent at all levels of biological organization. Crucially, conflicts and frustration result in self-organized criticality, emergence of new levels of selection and major transitions in evolution. I exemplify this conceptual framework with a key aspect in the evolution of life, the perennial coevolution between viruses and other mobile genetic elements (MGE) and their cellular hosts that involves both the obvious conflict and various forms of cooperation. All life forms host multiple MGE, and accordingly, all evolved defense systems that function via diverse mechanisms. I present evidence of tight evolutionary integration between MGE and host defense systems that transcends the proverbial arms race. To a different degree, defense systems themselves often behave like MGE. Key components of MGE, in particular, site-specific nucleases, are ‘guns for hire’ that are repeatedly recruited for defense functions. The evolutionary integration of MGE and defense systems that produces enormous diversity on both sides is a striking case of conflict-driven evolution that brings about cooperation and complexity..
December 15, 2020: (4PM via Zoom) – Corina Tarnita, Princeton University
The Ecology and Development of Social Aggregates: Insights From the Social Amoeba Dictyostelium discoideum.
Host: Archishman Raju
Cellular slime molds, including the well-studied Dictyostelium discoideum, are amoebae whose life cycle includes both a single-cellular and a multicellular stage. To achieve the multicellular stage, individual amoebae aggregate upon starvation to form a fruiting body made of dead stalk cells and reproductive spores, a process that has been described in terms of cooperation and altruism. When amoebae aggregate they do not perfectly discriminate against nonkin, leading to chimeric fruiting bodies. Within chimeras, apparent interactions among genotypes have been documented that should theoretically reduce genetic diversity. This is however inconsistent with the great diversity of genotypes found in nature. Loner cells that never participate in the aggregate have been theoretically proposed as a solution to this paradox, but this solution rests on the strong assumption that these loner cells are part of a mosaic of life-history strategies, and not just incidental byproducts of large-scale coordination attempts. I will provide empirical evidence of naturally occurring heritable variation in loner behavior in D. discoideum and use theoretical and experimental insights to propose a plausible mechanism by which such loners are generated. I will argue that loners could be critical to understanding collective and social behaviors, multicellular development, and ecological dynamics in D. discoideum and propose that, more broadly, across taxa, imperfect coordination of collective behaviors might be adaptive by enabling diversification of life-history strategies.
January 8, 2019, 2pm: Fellow Interview – Shyr-Shea Chang, UCLA
What are the physical principles of microvascular networks?
Host: E. Siggia
Microvascular networks are complex networks containing up to tens of thousands of blood vessels in a millimeter cube. While the main function of these networks is transporting oxygen and nutrients to tissues, there are other functions important to survival, such as transport efficiency and damage resistance, and it is not clear which functions are adaptive for microvascular networks. Here we start by looking at zebrafish trunk microvascular network. The fine vessels are like rungs of a ladder with different distances from the heart, yet the blood flows through them do not exponentially decrease as predicted by simple electric circuit approximation. The red blood cell temporarily occludes the fine vessel it passes through, and we found that this occlusion is tuned for achieving uniform blood flow in fine vessels. The uniform flow is maintained at the cost of transport efficiency, suggesting that microvascular networks might not be transport efficient. To further explore this hypothesis, we implemented a gradient descent algorithm that finds optimal networks for general functions and constraints, and we compared zebrafish trunk network with uniform flow network and transport efficient network. We found that the uniform flow network quantitatively describes the real zebrafish trunk network. When transport efficiency is imposed as a constraint instead of a target function, we discovered an abrupt change in network morphology when the burden of transport cost exceeds a threshold, suggesting that biological network always conforms to the most urgent constraint on it. Finally, I will talk about our recent work on a novel vessel adaptation mechanism based on sensing the large shear stress created by red blood cells, and compare our prediction to blood flow data in zebrafish embryos of different ages.
January 15, 2019, 2pm: Fellow Interview – Samhita Banavar, UCSB
Mechanical feedback coordinates yeast mating projection morphogenesis
Host: E. Siggia
All morphogenesis processes, whether at the cell scale or tissue level, require the coordination of growth and mechanics to properly shape functional structures. However, the mechanisms that coordinate these two processes in the sculpting of individual cells, and especially in walled cells, remain unknown. Using yeast mating projection growth as a model system, we show that a genetically-encoded mechanical feedback relays information about the mechanical state of the cell wall to the intracellular processes assembling it, thereby coordinating cell wall expansion and growth during cell morphogenesis. We find that mechanical feedback is essential to stabilize cell growth, but the shape and size of the cell are insensitive to the feedback and independently controlled. Beyond the role of mechanical feedback in cell viability during growth, cell wall mechanics may also inform other key cellular processes, such as cell polarization. While simulations of the cell polarization machinery can readily generate a stable polarization cap in a spherical geometry, maintenance of the polarization cap at the tip of the mating projection is not possible in realistic cell geometries. We show that the same mechanical feedback mechanism ensuring cell viability also results in the desired spatial coordination of the location of the polarization cap and the tip of the projection.
January 29, 2019, 2pm: Fellow Interview – Stuart Sevier, Rice University
Mechanical Properties of Transcription and their Role in Genome Structure and Memory
Host: E. Siggia
How is the physical state of DNA maintained so that the right genes are activated, controlled and silenced at the right time and in the right place? How do cells with the same genome di fferienate into cell types which display such di ferent behavior? How are those behaviors maintained? And how do they go wrong? Answering these questions has emerged as a grand challenge for molecular biology in the coming decade. In this talk, I will outline some of the basic physical elements of transcriptional initiation and elongation. A mathematical formulation of the ‘twin domain model’ of transcription will be presented with a focus on simple descriptions of each process. The resulting framework is combined with well-established stochastic processes of transcription resulting in a model which characterizes the impact of the mechanical properties of transcription on elements of gene expression and DNA structure. Importantly, this model opens a window onto the role of gene expression in shaping the storage and transfer of information through the physical and chemical state of DNA offering insight into a mechanical method of feedback for cells to regulate function.
February 7, 2019: Liam Holt, NYU
Control and consequences of the physical properties of the cell interior
Host: E. Siggia
Tens of thousands of biochemical reactions occur simultaneously in the cell. Small molecules are channeled through metabolic pathways at blistering speed. Giant complexes assemble to orchestrate transcription and translation. ATP fuels the active transport of organelles along microtubules, and actin networks drive membrane remodeling and agitate the cytoplasm. All of this occurs within a crowded cell interior that approaches the physical limits where jamming will occur. This extreme physical environment is both essential for life, and a potential liability. If cells become too dilute, they senesce and die. On the other hand, increased crowding eventually stalls growth. We found that the central growth regulator, mTORC1, is a crucial regulator of crowding. We also found that mechanical compression affects crowding and phase separation. We propose that perturbations to the physical properties of the cell interior through mechanical forces and the regulation of growth pathways play a crucial role in cell, developmental and disease biology, including cancer and neurodegeneration.
February 14, 2019: Otto Cordero, MIT
Microbial interactions and the assembly of micro-scale communities
Host: D. Zeevi
In this talk I will present work done in my lab showing how ecological interactions control the assembly and function of microbial communities at micro-scales. Using model marine particles composed of a variety of carbohydrate commonly found in the ocean, I will show how microbial interactions such as cross-feeding and social cheating control community dynamics, leading to rapid successions on particles. By comparing successions on different substrates we were able to cluster successional species into two types of functional groups, one type that is specific to each carbohydrate, and a type that substrate-unspecific and instead driven by crossfeeding interactions. We show that this simple logic can be exploited to predict the composition of communities on multiple carbohydrates as simple linear combinations of the compositions on single carbohydrates. Finally, I will focus on the substrate-specific group of chitin degraders to discuss ongoing work on the role of cooperative interactions, mediated by extracellular enzymes.
February 19, 2019: Markita Landry, UC Berkeley
Nanomaterials Engineering to Probe and Control Living Systems
Host: J. Nirody
Unique physical, chemical, and optical phenomena arise when materials are confined to the nano-scale. We are accustomed to making observations and predictions for the behavior of living systems on a macroscopic scale that is intuitive for the time and size scales of our day-to-day lives. However, the building blocks of life: proteins, nucleic acids, and cells, occupy different spatiotemporal scales. Our lab focuses on understanding and exploiting tunable optical and chemical properties of nanomaterials to access information about biological systems stored at the nano-scale. We present recent work on developing and implementing dopamine nanosensors to image dopamine volume transmission in the extracellular space of the brain striatum in both model (mouse) and non-model (bat) species. We validate our dopamine nanosensor in acute striatal slices with electrical and optogenetic stimulation of dopamine release, and show disrupted dopamine release or reuptake kinetics when brain tissue is exposed to dopamine agonist or antagonist drugs [1]. We characterize our findings in the context of their utility for high spatial and temporal neuromodulator imaging in the brain with 2-photon microscopy [2], and describe nanosensor exciton behavior from a molecular dynamics (MD) perspective [3]. We also discuss how high aspect ratio nanomaterials can be synthesized to carry biomolecular cargo to living systems. In particular, genetic engineering of plants is at the core of environmental sustainability efforts, but the physical barrier presented by the cell wall has limited the ease and throughput with which exogenous biomolecules can be delivered to plants. We will describe how nanomaterials engineering principles can be leveraged to manipulate living plants, in efforts to reconcile the benefits of crop genetic engineering with the demand for non-GMO foods [4]. Our work in the agricultural space provides a promising tool for species-independent, targeted, and passive delivery of genetic material, without transgene integration, into plant cells for rapid and parallelizable testing of plant genotype-phenotype relationships.
1. Beyene, A.B., McFarlane, I.R., Pinals, R.L, Landry, M.P.‡ Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space. ACS Chemical Neuroscience 8 (10), 2275-2289 (2017).
2. Del Bonis O’Donnell, J.T., Page, R.H., Beyene, A.G., Tindall, E.G., McFarlane, I.R., Landry, M.P.‡ Molecular Recognition of Dopamine with Dual Near Infrared Excitation-Emission Two-Photon Microscopy. Advanced Functional Materials (2017). DOI: 10.1002/adfm.201702112
3. Beyene, A.G., Delevich, K., Del Bonis-O’Donnell, J.T., Piekarski, D.J., Lin, W.C., Thomas, A.W., Yang, S.J., Kosillo, P., Yang, D., Wilbrecht, L., Landry, M.P. ‡ Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor. Nano Letters 18 (11), 6995-7003 (2018).
4. Demirer, G.S., Chang, R., Zhang, H., Chio, L., Landry, M.P.‡ Nanoparticle-Guided Biomolecule Delivery for Transgene Expression and Gene Silencing in Mature Plants. bioRxiv (2018). DOI: 10.1101/179549
1. Beyene, A.B., McFarlane, I.R., Pinals, R.L, Landry, M.P.‡ Stochastic Simulation of Dopamine Neuromodulation for Implementation of Fluorescent Neurochemical Probes in the Striatal Extracellular Space. ACS Chemical Neuroscience 8 (10), 2275-2289 (2017).
2. Del Bonis O’Donnell, J.T., Page, R.H., Beyene, A.G., Tindall, E.G., McFarlane, I.R., Landry, M.P.‡ Molecular Recognition of Dopamine with Dual Near Infrared Excitation-Emission Two-Photon Microscopy. Advanced Functional Materials (2017). DOI: 10.1002/adfm.201702112
3. Beyene, A.G., Delevich, K., Del Bonis-O’Donnell, J.T., Piekarski, D.J., Lin, W.C., Thomas, A.W., Yang, S.J., Kosillo, P., Yang, D., Wilbrecht, L., Landry, M.P. ‡ Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor. Nano Letters 18 (11), 6995-7003 (2018).
4. Demirer, G.S., Chang, R., Zhang, H., Chio, L., Landry, M.P.‡ Nanoparticle-Guided Biomolecule Delivery for Transgene Expression and Gene Silencing in Mature Plants. bioRxiv (2018). DOI: 10.1101/179549
February 26, 2019: Eva Kanso, USC
Unity and Diversity in the biological functions of cilia-driven flows
Host: J. Nirody
Motile cilia are micron-scale hair-like protrusions from epithelial cells that beat collectively to transport fluid. On the tissue level, cilia serve diverse biological functions, such as mucociliary clearance in the airways and cerebrospinal fluid transport in the brain ventricles. Yet, the relationship between the structure and organization of ciliated tissues and their biological function remains elusive. Here, I will present a series of mathematical and experimental models that examine: (1) the emergence of self-sustained oscillations in a single cilium, (2) the coordinated beating of neighboring cilia, and (3) the role of cilia-driven flows in particle transport, mixing, capture and filtering. I will conclude by commenting on the implications of these models to understanding the biophysical mechanisms underlying the interaction of ciliated tissues with microbial partners.
March 5, 2019: Sean Eddy, Harvard
Peter H. Sellers Lecture: Sequence homology searches: the future of deciphering the past
Host: J. Nirody
Carson Family Auditorium, B-Level, Greenberg Building
Carson Family Auditorium, B-Level, Greenberg Building
Computational recognition of distant sequence homology is a key to studying ancient events in molecular evolution. The better our sequence analysis methods are, the deeper in evolutionary time we can see. A major aim in the field is to improve the resolution of homology recognition methods by building increasingly realistic, complex, parameter-rich models. I will describe current and future research in homology search algorithms based on probabilistic inference methods, including hidden Markov models (HMMs) and stochastic context-free grammars (SCFGs). We make these methods available in the HMMER and Infernal software from my laboratory, in collaboration with sequence family databases including Pfam and Rfam.
March 12, 2019: Thierry Emonet, Yale
Conflicts and synergies between phenotypic heterogeneity and collective migration
Host: J. Nirody
Phenotypic diversity and collective behavior are important properties of living communities that provide selective advantages. While collective behavior requires coordination between individuals, phenotypic diversity tends to reduce coordination. I will report on our recent experimental and theoretical results that used bacterial chemotaxis as model system to examine how phenotypic heterogeneity modulates group performance and how collective behavior affects the amount of diversity in the group.
This study was supported by the National Institutes of Health grant R01GM106189, the Allen Distinguished Investigator Program (grant 11562) through The Paul G. Allen Frontiers Group, and the James S. McDonnell Foundation grant on Complexity.
This study was supported by the National Institutes of Health grant R01GM106189, the Allen Distinguished Investigator Program (grant 11562) through The Paul G. Allen Frontiers Group, and the James S. McDonnell Foundation grant on Complexity.
March 19, 2019: Richard Bonneau, NYU
Inference of regulatory networks from single-cell and spatial transcriptomics data: new methods and a new benchmark dataset.
Host: J. Nirody
I will briefly describe scalable models for regulatory network inference that enable multiple study and multiple cell-type comparisons, include some of the key biophysics of transcriptional dynamics, and can be adapted to the latest single-cell and spatial genomics technologies. I will focus on new developments to enable learning networks from single cell and spatial transcriptomics. A key challenge in single-cell and spatial transcriptomics data is that many heterogenous cell types are measured in each experiment; to address this heterogeneity we adapt multi-task approaches to network inference to the context of single cell data. Another key challenge with spatial and single-cell based network inference is the lack of appropriate benchmarks. To address this lack of a suitable benchmark dataset, we (with David Gresham lab) have generated a new single-cell RNA sequencing data set for the model species Saccharomyces cerevisiae (there are many thousands of well characterized regulatory and signaling interactions for this well studied model system), which contains 40,000 individual cells (cells grown in 11 different environmental growth conditions and on multiple genetic backgrounds; TF knockouts). These new scRNA-seq datasets are integrated with ATAC-seq and other genomic resources to adapt and benchmark our network inference tools. We will also provide this dataset to the computational community for tool development any day now. These methods are currently being applied to work on the mammalian immune system, cancer, mouse intercortical neuron development and the spatiotemporal genomics of human and mouse spinal column.
March 28, 2019: Thomas Fai, Brandeis
Length regulation of multiple flagella that self-assemble from a shared pool of components
Host: J. Nirody
The single cell biflagellate Chlamydomonas reinhardtii has proven to be a very useful model organism for studies of size control. The lengths of its two flagella are tightly regulated. We study a model of flagellar length control whose key assumption is that proteins responsible for the intraflagellar transport (IFT) of tubulin are present in limiting amounts. In the case of two simultaneously assembling flagella, regardless of the details of how the flagella are coupled, we find that the widely-used assumption of a constant disassembly rate is inconsistent with experimental results. We therefore propose a model in which diffusion gives rise to a length-dependent concentration of depolymerizer at the flagellar tip. This model is found to be consistent with experimental results and generalizes to other situations such as arbitrary flagellar number.
April 2, 2019: William Ryu, University of Toronto
The Physics of Behavior: measuring and modeling the sensorimotor response of C. elegans.
Host: J. Nirody
One of the grand goals in science is to understand how neural, genetic, and biochemical circuits produce behavior. While a great deal of work has been done in the development of tools to perturb and measure the circuits underlying sensory behavior, advances in the study of behavior itself has lagged behind. For this talk I will describe some attempts to close this gap with focus on C. elegans locomotion and its response to thermal stimuli. The roundworm C. elegans is a simple organism with only 300 neurons but it can generate complex adaptive behavioral responses to a wide range of sensations including taste, touch, and temperature. C. elegans behavior consists of a number of stereotyped locomotory states such as forward and reverse, pausing, turning, etc. Typically, the worm moves by making stochastic transitions between these states, and in response to sensory measurements it adapts by biasing the probability of these transitions. However, under certain conditions the worm will transition from stochastic to deterministic behavior such as in response to sensory gradients or noxious or “painful” stimuli. We have developed simple desktop experiments to quantitatively capture the behavioral responses of C. elegans to precise thermal stimuli. Using these data we have shown that C. elegans moves through a “shape space” that is low dimensional in which four dimensions capture approximately 95% of the variance in body shape. Here I will give two examples of modeling that take advantage of this low dimensionality and stereotypy. In the first we show that stochastic dynamics within this shape space predicts transitions between attractors corresponding to abrupt reversals in crawling direction. With no free parameters, our inferred stochastic dynamical system generates reversal timescales and stereotyped trajectories in close agreement with experimental observations. In the second we use Sir Isaac, an algorithm that allows inference of the dynamical equations underlying a noisy time series, even if the dynamics are nonlinear—to analyze the thermal “pain” response of C. elegans. Both examples show that it is possible to learn “equations of behavior” of the worm, and that these equations give an interpretable, complementary perspective to traditional biological studies.
April 9, 2019: Padmini Rangamani, UCSD
Geometric principles of spatio-temporal dynamics of second messengers in dendritic spines
Host: J. Nirody
The ability of the brain to encode and store information depends on the plastic nature of the individual synapses. The increase and decrease in synaptic strength, mediated through the structural plasticity of the spine, are important for learning, memory, and cognitive function. Dendritic spines are small structures that contain the synapse. They come in a variety of shapes (stubby, thin, or mushroom-shaped) and a wide range of sizes that protrude from the dendrite. These spines are the regions where the postsynaptic biochemical machinery responds to the neurotransmitters. Spines are dynamic structures, changing in size, shape, and number during development and aging. While spines and synapses have inspired neuromorphic engineering, the biophysical events underlying synaptic and structural plasticity remain poorly understood.
Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss two recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and second, a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.
Our current focus is on understanding the biophysical events underlying structural plasticity. I will discuss two recent efforts from my group — first, a systems biology approach to construct a mathematical model of biochemical signaling and actin-mediated transient spine expansion in response to calcium influx caused by NMDA receptor activation and second, a series of spatial models to study the role of spine geometry and organelle location within the spine for calcium and cyclic AMP signaling. I will conclude with some new efforts in using reconstructions from electron microscopy to inform computational domains. I will conclude with how geometry and mechanics plays an important role in our understanding of fundamental biological phenomena and some general ideas on bio-inspired engineering.
April 16, 2019: Kenny Breuer, Brown
Bacterial Swimming in Viscous and Viscoelastic Fluids
Host: J. Nirody
There has long been controversy and conflicting data regarding the speed at which flagellated bacteria swim in fluids of different Newtonian and non-Newtonian properties. Some data indicates faster swimming in viscoelastic media, while others suggest slower swimming. There is even controversy regarding the swimming speed in purely Newtonian media of different viscosities. We will present data and analysis from recent experiments in which we follow smooth-swimming and wild-type E coli using a tracking microscope as they move through different Newtonian and non-Newtonian fluids. We find that increasing the bulk viscosity affects swimming speed in two ways – it reduces the effectiveness of flagellar propulsion, but also increases the flagellar bundling time of wild type E coli. Cells are found to swim faster in non-Newtonian fluids, even though the bulk viscosity rises. The dominate contribution is found to be related to the strong shear thinning property of the fluid, although the normal stresses associated with viscoelasticity can also contribute through reduced cell precession, slightly improved propulsive efficacy and faster flagellar bundling times.
April 23, 2019: Lars Dietrich, Columbia
The interplay of metabolism and structure in microbial biofilms
Host: J. Nirody
Studies of signaling cascades can reveal important mechanisms driving multicellular development, but the models that emerge often lack critical links to environmental cues and metabolites. We study the effects of extra- and intracellular chemistry on biofilm morphogenesis in the pathogenic bacterium Pseudomonas aeruginosa, which produces oxidizing pigments called phenazines. While wild-type colonies are relatively smooth, phenazine-null mutant colonies are wrinkled. Initiation of wrinkling coincides with a maximally reduced intracellular redox state, suggesting that wrinkling is a mechanism for coping with electron acceptor limitation. Mutational analyses and in situ expression profiling have revealed roles for PAS-domain and other redox-sensing regulatory proteins, as well as genes involved in motility and matrix production, in colony morphogenesis. To characterize endogenous electron acceptor production, we have developed a novel chip that serves as a growth support for biofilms and allows electrochemical detection and spatiotemporal resolution of phenazine production in situ. Through these diverse approaches, we are developing a broad picture of the mechanisms and metabolites that exert an integrated influence over redox homeostasis in P. aeruginosa biofilms
April 24, 2019, 2-3:30p: Two speakers
Host: A. Libchaber
Vincent Noireaux, University of Minnesota
Cell-free expression systems: from the genetic code to synthetic cells
Cell-free expression has crossed the ages as a tool to address fundamental questions in biology and as an experimental platform to prototype biochemical systems. In the last decade, a new post-genomic generation of cell-free expression systems has emerged. In vitro protein synthesis has become powerful, versatile and scalable. Larger and larger DNA programs can be executed in vitro to construct living systems from scratch. Cell-free expression has also been devised to expand the capabilities of natural cells. In this talk, I will present the current field of cell-free constructive biology and show several examples of current capabilities, including prototyping CRISPR technologies and assembling synthetic cells.
Yusuke Maeda, University of Tokyo
On-chip membrane-bound TXTL as minimal cells
Artificial cells made of molecular components and lipid membrane are emerging platforms to characterize, by construction, the properties of living systems. Cell-free transcription-translation (TXTL) system offers several advantages for the bottom-up synthesis of cellular reactors. Yet, scaling up their design within well-defined geometries remains challenging. We present a microfluidic device hosting TXTL reactions of a single reporter gene in thousands of microwells separated from an external buffer by a phospholipid membrane. The yield of the protein synthesis in sealed micro-reactors exhibits a bimodal distribution of active and inactive states, in spite of the absence of transcriptional feedback regulation. Adding fatty acid and polymer in buffer increases the membrane stability along with the proportion of active micro-reactors. Protein synthesis is greater in micro-reactors with stable membranes. This indicates that boundary plays regulatory role on cell-free TXTL systems as a part of minimal artificial cells.
May 7, 2019: Liana Lareau, UC Berkeley
Ribosome dynamics captured by deep sequencing and deep learning
Host: J. Nirody
Synonymous codon choice can have dramatic effects on ribosome speed, RNA stability, and protein expression. Ribosome profiling experiments have underscored that ribosomes do not move uniformly along mRNAs, exposing a need for models of translation that capture the full range of empirically observed variation. Previously, we showed that deep sequencing of ribosome-protected mRNA fragments reveals not only the position of each ribosome but also, unexpectedly, its particular stage of the elongation cycle. This provides a new way to study the detailed kinetics of translation and a new probe with which to identify sequence elements that affect each step in the elongation cycle. More recently, we modeled variation in translation elongation using a feedforward neural network to predict the ribosome density at each codon as a function of its sequence neighborhood. We applied our model to design synonymous variants of a fluorescent protein in yeast and concluded that control of translation elongation alone is sufficient to produce large, quantitative differences in protein output.
May 14, 2019: Robert Endres, Imperial College, London
Chemotaxis: linking cell shape, behavior, and strategy
Host: J. Nirody
The behavior of an organism often reflects a strategy for coping with its environment. Such behavior in higher organisms can often be reduced to a few stereotyped modes of movement due to physiological limitations, but finding such modes in amoeboid cells is more difficult as they lack these constraints. In this talk, I will examine cell shape and movement in starved Dictyostelium amoebae during single-cell and collective cell migration. I will show that the incredible variety in amoeboid shape across a population can be reduced to a few modes of variation, and that different modes are used depending on the steepness of the applied chemical gradient or drug treatment. The origins of cell shape and behavior are tackled by both forward and inverse modeling, predicting long-term cell behavior without reference to biochemical details. Analogies with statistical mechanics are made throughout the talk.
May 21, 2019: Christian Machens, Champalimaud Foundation
An alternative view of what neural circuits may be doing
Host: J. Nirody
The classical picture of a neural network assumes that each neuron sums its inputs, followed by a nonlinear activation function. Interesting computations emerge by combining and wiring up these individual units. While immensely successful (both in neuroscience and AI), this view has also created several persistent puzzles about the organization of neural systems. One puzzle are spikes, which have largely remained a nuisance, rather than a feature of neural systems. Another puzzle is robustness to perturbations, which is ubiquitous in biology, but largely ignored in neural network modeling. I will show that these puzzles can be resolved if we shift our perspective of how neural systems operate. Based on only two assumptions – that the effective output of a neural network can be extracted via linear readouts, and that each neuron only fires to bound an error on this output, I will show how to derive a spiking network of integrate-and-fire neurons that exhibits irregular and asynchronous spike trains, balance of excitatory and inhibitory currents, and robustness to perturbations. I will provide geometric intuitions for the network’s functionality, and use these insights to show how these networks can control the precision of their output via bottom-up and top-down gain modulation, even though they consist of integrate-and-fire neurons only.
September 10, 2019: Tami Lieberman, MIT
De novo mutations in human gut and skin microbiomes
Host: D. Zeevi
There is an enormous potential for evolution within each of our microbiomes, with billions of new mutations being created each day. In this talk, I will highlight the power of tracking within-person evolution for understanding bacterial transmission, identifying genes critical to long-term survival, and for understanding evolutionary principles. I will present examples from infectious diseases, the gut microbiome, and the skin microbiome, and speculate on how within-person evolution may impact and inform microbiome-targeted therapies.
Relevant DOIs: 10.1016/j.chom.2019.03.007, 10.1038/ng.997
Relevant DOIs: 10.1016/j.chom.2019.03.007, 10.1038/ng.997
September 17, 2019: Raphael Turcotte, University of Oxford
Optical microscopy with wavefront control for neuroscience
Host: J. Nirody
Optical microscopy has become an essential tool for neuroscientists. The primary reason for this success is that light enables the interrogation of living tissue in its physiological context at the length-scale of neurons and their subcellular components. Nevertheless, the quality of optical images is depth-dependent, because biological samples induce aberrations and scatter light, which ultimately limits imaging depth. In this talk, I will discuss how wavefront control (WC), an approach enabling the sculpting of light, can alleviate these issues for brain imaging in live animals for several imaging modalities. First, I will show that WC can be implemented for two-photon fluorescence imaging to improve the quality of structural imaging and the accuracy of functional measurements. Then, I will illustrate how WC is essential for achieving super-resolution imaging (structured illumination microscopy) in the living brain. Finally, I will examine how WC plays an essential role in minimally invasive micro-endoscopy with multimode optical fibers and thus enables subcellular imaging centimeters into the brain in vivo.
September 24, 2019: Zahra Fakhraai, University of Pennsylvania
Surface-mediated peptide self-assembly to modulate surface energy
Host: J. Nirody
Amyloid-forming peptides or proteins typically form amyloid fibrils through a nucleation and growth mechanism. The nucleation step typically requires a minimum concentration of peptides in the solution, thus preventing this one-dimensional crystallization process from moving forward in dilute solutions. We demonstrate that in a dilute solution, where bulk nucleation and growth is limited, providing the right surface can promote rapid self-assembly of peptides into mono-layer thick amyloid fibrils within minutes. This process is diffusion-limited and depends on the orientation and diffusion of peptides on the surface. The deposition rate of peptides, as well as their orientation and diffusion on the surface strongly depend on the strength of the peptide-surface interactions. The self-assembly can be suppressed either by reducing the interaction such that the deposition rate is reduced or by increasing the interaction energy to a point where the peptide diffusion is substantially reduced.
Once the self-assembly moves forward with time, the side-chains of the residues can modulate the surface energy and thus the interaction energy between the incoming peptides and the effective surface. As such, depending on the nature of the side chains, the surface can become more hydrophobic or hydrophilic and promote or prevent the further deposition of the peptides. We demonstrate the generality of using this method to modulate the effective surface energy by the desired amount through the design of side chains, thus engineering surfaces with self-limiting self-assemblies with specific surface energy values.
Once the self-assembly moves forward with time, the side-chains of the residues can modulate the surface energy and thus the interaction energy between the incoming peptides and the effective surface. As such, depending on the nature of the side chains, the surface can become more hydrophobic or hydrophilic and promote or prevent the further deposition of the peptides. We demonstrate the generality of using this method to modulate the effective surface energy by the desired amount through the design of side chains, thus engineering surfaces with self-limiting self-assemblies with specific surface energy values.
October 1, 2019: Yuhai Tu, IBM
Optimal coding strategies in the peripheral olfactory systems: Compressed sensing for an array of nonlinear olfactory receptor neurons with and without spontaneous activity
Host: J. Nirody
The peripheral olfactory systems are capable of coding sparse mixtures of a few odorants in a high dimensional space (the number of possible odorant molecules is huge) by using a relatively small number of olfactory receptor neurons. Data compression is also an important problem in computer science, where powerful algorithms have been developed for compressing sparse high-dimensional data. In particular, the compressed sensing (CS) theory has been successfully used to compress high-dimensional information efficiently by exploiting the sparsity of the signal. However, the much-celebrated CS theory/algorithm requires the sensors to be linear. For neural sensory systems such as the olfactory system, the receptor neurons (sensors) respond nonlinearly to odorant concentration and have a finite response range. Therefore, the CS algorithm does not apply to sensory systems directly, and the question on how olfactory systems compress information remains open.In this talk, we will present some recent results on how a relatively small number of nonlinear sensors each with a limited response range can optimize transmission of high dimensional sparse odor mixture information. For neurons without spontaneous activity, we found that the optimal coding matrix is sparse — only a subset of neurons respond to a given odorant with their sensitivities following a broad (such as log-normal) distribution matching the odor mixture statistics. We showed that this maximum entropy code enhances the performances of the downstream reconstruction and classification tasks. For neurons with a finite spontaneous (basal) activity, our study showed that introducing odor-evoked inhibition further enhances coding capacity and the fraction of inhibitory interactions for each neuron increases with its basal activity. Comparisons with available experiments in olfactory systems are consistent with our theory.
October 10, 2019: Georg Gerber, Harvard University
Computational Biology and the Microbiome: Discovery and Prediction for Microbe-based Therapeutics and Diagnostics
Host: D. Zeevi
The human microbiome is highly dynamic on multiple timescales, changing dramatically during development of the gut in childhood, with diet, or due to medical interventions. Understanding and being able to manipulate these dynamics is essential for the rational design of microbiome-based diagnostics and therapeutics. However, analysis of longitudinal microbiome data is hampered by a paucity of tailored and principled computational methods that address inherent challenges of these data including temporally irregular and sparse sampling, experimental noise, and complex dependency structures. I will present several novel Bayesian machine learning methods that we have developed to overcome these challenges. The first, MC-TIMME (Microbial Counts Trajectories Infinite Mixture Model Engine), is a non-parametric Bayesian model for clustering microbiome time-series data that we have applied to gain insights into the temporal response of human and animal microbiota to antibiotics, infectious, and dietary perturbations. The second, MDSINE (Microbial Dynamical Systems INference Engine), is a method for efficiently inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors of the microbiota, which we have applied to developing bacteriotherapies for C. difficile infection and inflammatory bowel disease. The third, Microbiome Interpretable Temporal Rule Engine (MITRE), is a method for predicting host status from microbiome time-series data, which achieves high accuracy while maintaining interpretability by learning predictive rules over automatically inferred time-periods and phylogenetically related microbes. We are currently using MITRE to analyze data from human cohorts, specifically for developing a microbiome-based diagnostic for C. difficile recurrence.
October 17, 2019: Oskar Hallatschek, Berkeley
Microbes under pressure
Host: J. Nirody
In natural settings, microbes tend to grow in dense populations where they need to push against their surroundings to accommodate space for new cells. The associated contact forces play a critical role in a variety of population-level processes, including biofilm formation, the colonization of porous media, and the invasion of biological tissues. Although mechanical forces have been characterized at the single cell level, it remains elusive how single-cell forces combine to generate population-level patterns. I present a synthesis of theory and microbial experiments that show that contact forces generated by microbial populations can become very large due to a self-driven jamming mechanism. These forces feed back on the physiology of the cells and can strongly perturb the mechanical integrity of the environment, thereby promoting microbial invasion. Finally, I highlight that the cooperative nature of microbial force generation induces a screening effect that reduces the selection against slower growing mutant types. These results underscore that, in crowded microbial populations, collective phenomena often have a mechanical basis.
October 29, 2019: Anne Dekas, Stanford University
A single-cell view of microbial activity in the dark ocean
Host: D. Zeevi
The dark ocean is one of the largest habitats for microbial life on the planet: it covers nearly two thirds of our Earth’s surface and harbors well over half of marine microorganisms. The activity of microorganisms in the deep sea plays an essential role in biogeochemical cycling, including the production and consumption of greenhouse gases (e.g., CH4, CO2 and N2O), thereby affecting climate. The goal of my research is to understand the activity of bacteria and archaea in the dark ocean: who is doing what, how much, and what affects metabolic rates? In this talk, I will describe two lines of research, one investigating nitrogen fixation in deep-sea sediments, and one probing organic substrate utilization by pelagic marine Thaumarchaeota. Additionally, I will describe the culture-independent techniques we employ, including recent methodological advances in the use of nanoscale secondary ion mass spectrometry (nanoSIMS) to quantify anabolic activity in uncultured microorganisms on the single-cell level.
November 7, 2019: Smita Krishnaswamy, Yale University
Detection structure and patterns in big biomedical data
Host: D. Zeevi
High-throughput, high-dimensional data has become ubiquitous in the biomedical and health sciences as a result of breakthroughs in measurement technologies like single cell RNA-sequencing, as well as vast improvements in health record data collection and storage. While these large datasets containing millions of cellular or patient observations hold great potential for understanding generative state space of the data, as well as drivers of differentiation, disease and progression, they also pose new challenges in terms of noise, missing data, measurement artifacts, and the so-called “curse of dimensionality.” In this talk, I will cover a unifying theme in my research which has helped to generally tackle these problems: manifold learning and the associated manifold assumption. The manifold assumption in the data analysis context refers to the idea that while the ambient measurement space is high dimensional and noisy, that the intrinsic state space lies in lower dimensional smoothly varying patches that are locally Euclidean, called manifolds. In my work, I learn the data manifold using two types of techniques: graph signal processing and deep learning. Manifold learning provides a powerful structure for algorithmic approaches to denoise the data, visualize the data and understand progressions, clusters and other regulatory patterns, as well as correct for batch effects to unify data. I will cover several applications of this principle via specific projects including: 1) MAGIC: a manifold denoising algorithm that low-pass filters data features (like audio and video signals are denoised) on a data graph, for denoising and recovery of cellular data. 2) PHATE: a general visualization and dimensionality reduction technique technique that offers an alternative to tSNE in that it preserves local and global structures, clusters as well as progressions using an information-theoretic distance between diffusion probabilities. 3) MELD: an analysis technique for comparing two or more experiments measuring the same underlying system (i.e., cells from the same type of tissue) that produces a continuously varying likelihood score throughout the manifold to indicate whether each position in the state space is enriched in one of the specific conditions. This technique is useful for pulling out subtle differences in response between different drug treatments or experimental conditions in large datasets. 4) SAUCIE (Sparse AutoEncoders for Clustering Imputation and Embedding), our highly scalable neural network architecture that simultaneously performs denoising, batch normalization, clustering and visualization via custom regularizations on different hidden layers. Finally, I will preview ongoing work in neural network architectures for predicting dynamics and other biological tasks.
November 12, 2019: Karen Kasza, Columbia University
Building the embryo: mechanisms controlling tissue flows during development
Host: J. Nirody
During embryonic development, groups of cells reorganize into functional tissues with complex form and structure. Tissue reorganization can be rapid and dramatic, often occurring through embryo-scale flows that are mediated by the coordinated actions of cells. In Drosophila embryos, cell rearrangements in the epithelium rapidly narrow and elongate the tissue, doubling the length of the body axis in just 30 minutes. This type of tissue movement is highly conserved and can be driven by internal forces generated by cells or external forces from neighboring tissues. While much is known about the molecules involved in these cell and tissue movements, it is not yet clear how these molecules work together to coordinate cell behaviors and generate coherent movements at the tissue-scale. To gain mechanistic insight into this problem, my lab combines genetic and biophysical approaches with emerging optogenetic technologies for manipulating molecular and mechanical activities in cells with high precision. I will discuss some of our recent findings on how cell properties and interactions are regulated in the Drosophila embryo to allow (or prevent) rapid cell rearrangement and tissue flow during specific developmental events.
November 18, 2019: Erik van Nimwegen, University of Basel
Inferring gene regulatory dynamics from single-cell data
Host: E. Siggia
I plan to discuss a new method for inferring, from scRNA-seq data, transcription regulatory interactions that guide single-cell gene expression trajectories. The method combines three new ideas: First, a scRNA-seq normalization method that rigorously deconvolves sampling noise from true variations in transcription rates. Second, a Bayesian method that infers the ‘regulatory states’ of each single cell by modeling measured transcription rates in terms of genome-wide computational predictions of transcription factor binding sites. And third, a maximum entropy approach that infers an effective epigenetic landscape that guides the distribution of single cells in the space of regulatory states. Time permitting I will briefly mention studies of our lab on single-cell gene regulation in bacteria using a combination of microfluidics and time-lapse microscopy. These studies highlight how gene regulation is not only strongly coupled to fluctuations in the physiological state of cells but that, by propagation of noise through the regulatory network, expression noise and gene regulation are intimately entangled.
December 3, 2019: Tamar Schlick, NYU
Folding Genes at Nucleosome Resolution
Host: J. Nirody
Deciphering chromosome tertiary organization is essential for understanding how genetic information is replicated, transcribed, silenced, and edited to control basic life processes. Many experimental studies of chromatin using nucleosome structure determination, ultra-structural techniques, single-force extension studies, and analysis of chromosomal interactions have revealed important chromatin characteristics as a function of various internal and external conditions, such as looping, compaction, and compartmentalization. Modeling studies, anchored to high-resolution nucleosome models, have explored related questions systematically. In this talk, I will describe multiscale computational approaches for chromatin modeling at nucleosome resolution and recent mesoscale chromatin simulations that incorporate key physical parameters such as nucleosome positions, linker histone binding, and acetylation marks to ‘fold’ in silico the Hox C gene cluster. The folded gene reveals a contact hub that connects an acetylation-rich with a linker histone-rich region. Such chromatin modeling techniques open the way to other computational folding of genes and genomes. Moreover, the resulting folded system emphasizes the heterogeneity of chromatin fibers and hierarchical looping motifs, and underscores how nucleosome positions in combination with epigenetic marks and linker histone binding direct the tertiary folding of fibers and genes to perform their cellular tasks. These chromatin architecture findings have important implications on many important processes including cell differentiation, gene regulation, and disease progression.
Of possible interest:
G. Ozer, A. Luque and T. Schlick, “The Chromatin Fiber: Multiscale Problems and Approaches”, Curr. Opin. Struc. Biol. 31: 124–139 (2015).
S. Grigoryev, G. Bascom, J. M. Buckwalter, M. Schubert, C. L. Woodcock, and T. Schlick, “Hierarchical Looping of Zigzag Nucleosome Chains in Metaphase Chromosomes”, Proc. Natl. Acad. Sci. USA 113: 1238–1243 (2016).
G. Bascom, K. Sanbonmatsu, and T. Schlick, “Mesoscale Modeling Reveals Hierarchical Looping of Chromatin Near Gene Regulatory Elements”, J. Phys. Chem. B Special Issue: J. Andrew McCammon Festschrift 120: 8642–8653 (2016).
G. Bascom and T. Schlick, “Linking Chromatin Fibers to Gene Folding by Hierarchical Looping”, Biophys. J. 112: 434—445 (2017).
G. Bascom, T. Kim, and T. Schlick, “Kilobase Pair Chromatin Fiber Contacts Promoted by Living-System-Like DNA Linker Length Distributions and Nucleosome Depletion”, J. Phys. Chem. B Special Issue in Memory of Klaus Schulten 121 (15): 3882–3894 (2017).
S. S. P. Rao, S.-C Huang, B. G. St. Hilaire, J. M. Engreitz, E. M. Perez, K.-R Kieffer-Kwon, A. L. Sanborn, S. E. Johnstone, G. D. Bascom, I. D. Bochkov, X. Huang, M. S. Shamim, J. Shin, D. Turner, A. D. Omer, J. T. Robinson, T. Schlick, B. E. Bernstein, R. Casellas, E. Lander, and E. Lieberman-Aiden, “Cohesin Loss Eliminates All Loop Domains, Leading to Links Among Superenhancers and Downregulation of Nearby Genes”, Cell 171: 305–320 (2017).
G. Bascom, C. Myers, and T. Schlick, “Mesoscale Modeling Reveals Formation of an Epigenetically Driven HOXC Gene Hub”, Proc. Natl. Acad. Sci. USA 116: 4955–4962 (2019). (doi: https://doi.org/10.1073/pnas.1816424116). Accompanying commentary by Michele D. Pierro, “Inner Workings of Gene Folding”, Proc. Natl. Acad. Sci. USA 116: 4774–4775 (2019).
O. Perisic, S. Portillo-Ledesma, and T. Schlick, “Sensitive Effect of Linker Histone Binding Mode and Subtype on Chromatin Condensation”, Nuc. Acids Res., doi: 10.1093/nar/gkz234 (2019).
Of possible interest:
G. Ozer, A. Luque and T. Schlick, “The Chromatin Fiber: Multiscale Problems and Approaches”, Curr. Opin. Struc. Biol. 31: 124–139 (2015).
S. Grigoryev, G. Bascom, J. M. Buckwalter, M. Schubert, C. L. Woodcock, and T. Schlick, “Hierarchical Looping of Zigzag Nucleosome Chains in Metaphase Chromosomes”, Proc. Natl. Acad. Sci. USA 113: 1238–1243 (2016).
G. Bascom, K. Sanbonmatsu, and T. Schlick, “Mesoscale Modeling Reveals Hierarchical Looping of Chromatin Near Gene Regulatory Elements”, J. Phys. Chem. B Special Issue: J. Andrew McCammon Festschrift 120: 8642–8653 (2016).
G. Bascom and T. Schlick, “Linking Chromatin Fibers to Gene Folding by Hierarchical Looping”, Biophys. J. 112: 434—445 (2017).
G. Bascom, T. Kim, and T. Schlick, “Kilobase Pair Chromatin Fiber Contacts Promoted by Living-System-Like DNA Linker Length Distributions and Nucleosome Depletion”, J. Phys. Chem. B Special Issue in Memory of Klaus Schulten 121 (15): 3882–3894 (2017).
S. S. P. Rao, S.-C Huang, B. G. St. Hilaire, J. M. Engreitz, E. M. Perez, K.-R Kieffer-Kwon, A. L. Sanborn, S. E. Johnstone, G. D. Bascom, I. D. Bochkov, X. Huang, M. S. Shamim, J. Shin, D. Turner, A. D. Omer, J. T. Robinson, T. Schlick, B. E. Bernstein, R. Casellas, E. Lander, and E. Lieberman-Aiden, “Cohesin Loss Eliminates All Loop Domains, Leading to Links Among Superenhancers and Downregulation of Nearby Genes”, Cell 171: 305–320 (2017).
G. Bascom, C. Myers, and T. Schlick, “Mesoscale Modeling Reveals Formation of an Epigenetically Driven HOXC Gene Hub”, Proc. Natl. Acad. Sci. USA 116: 4955–4962 (2019). (doi: https://doi.org/10.1073/pnas.1816424116). Accompanying commentary by Michele D. Pierro, “Inner Workings of Gene Folding”, Proc. Natl. Acad. Sci. USA 116: 4774–4775 (2019).
O. Perisic, S. Portillo-Ledesma, and T. Schlick, “Sensitive Effect of Linker Histone Binding Mode and Subtype on Chromatin Condensation”, Nuc. Acids Res., doi: 10.1093/nar/gkz234 (2019).
December 10, 2019: Kelley Harris, University of Washington
Evolution of the mutation rate and spectrum in diverging human and ape populations
Host: J. Nirody
Recent studies of hominoid variation have shown that mutation rates and spectra can evolve rapidly, contradicting the fixed molecular clock model. The relative mutation rates of three-base-pair motifs differ significantly among great ape lineages, implying that multiple unknown modifiers of DNA replication fidelity have arisen and fixed on each branch of the ape phylogeny. Such mutator alleles might directly modify DNA replication or repair, or might instead act indirectly by modifying traits like reproduction or chromatin structure. Certain mechanisms of action are expected to create mutations in specific regions of the genome, meaning that the spatial distribution of lineage-specific mutations is informative about their causality. To harness this source of information, we measured mutation spectra of several functional compartments (such as late-replicating regions) whose attributes are known or suspected to affect their mutation rates. Using genetic diversity from 88 great apes, we find that most functional compartments are imprinted by localized mutational signatures but that these signatures explain very little of the mutational divergence between species. Rather, compartment-specific signatures layer with species-specific signatures to create mutational portraits that reflect both lineage and function. In particular, we identify a mutation signature enriched in endogenous retroviruses that seems to co-segregate with the experimentally-measured intensity of the hydroxymethylation of retrovirus-derived DNA. Our results suggest that cis-acting mutational modifiers are highly conserved between species and rapid mutation spectrum evolution is driven primarily by trans-acting modifiers.
January 9, 2018: Dan Yamins, Stanford University
Using artificial-intelligence-driven deep neural networks to uncover principles of brain representation and organization
Host: C. Kirst
Human behavior is founded on the ability to identify meaningful entities in complex noisy data streams that constantly bombard the senses. For example, in vision, retinal input is transformed into rich object-based scenes; in audition, sound waves are transformed into words and sentences. In this talk, I will describe my work using computational models to help uncover how sensory cortex accomplishes these enormous computational feats.
The core observation underlying my work is that optimizing neural networks to solve challenging real-world artificial intelligence (AI) tasks can yield predictive models of the cortical neurons that support these tasks. I will first describe how we leveraged recent advances in AI to train a neural network that approaches human-level performance on a challenging visual object recognition task. Critically, even though this network was not explicitly fit to neural data, it is nonetheless predictive of neural response patterns of neurons in multiple areas of the visual pathway, including higher cortical areas that have long resisted modeling attempts. Intriguingly, an analogous approach turns out be helpful for studying audition, where we recently found that neural networks optimized for word recognition and speaker identification tasks naturally predict responses in human auditory cortex to a wide spectrum of natural sound stimuli, and help differentiate poorly understood non-primary auditory cortical regions. Together, these findings suggest the beginnings of a general approach to understanding sensory processing the brain.
I’ll give an overview of these results, explain how they fit into the historical trajectory of AI and computational neuroscience, and discuss future questions of great interest that may benefit from a similar approach.
The core observation underlying my work is that optimizing neural networks to solve challenging real-world artificial intelligence (AI) tasks can yield predictive models of the cortical neurons that support these tasks. I will first describe how we leveraged recent advances in AI to train a neural network that approaches human-level performance on a challenging visual object recognition task. Critically, even though this network was not explicitly fit to neural data, it is nonetheless predictive of neural response patterns of neurons in multiple areas of the visual pathway, including higher cortical areas that have long resisted modeling attempts. Intriguingly, an analogous approach turns out be helpful for studying audition, where we recently found that neural networks optimized for word recognition and speaker identification tasks naturally predict responses in human auditory cortex to a wide spectrum of natural sound stimuli, and help differentiate poorly understood non-primary auditory cortical regions. Together, these findings suggest the beginnings of a general approach to understanding sensory processing the brain.
I’ll give an overview of these results, explain how they fit into the historical trajectory of AI and computational neuroscience, and discuss future questions of great interest that may benefit from a similar approach.
January 16, 2018: Michael Murrell, Yale
From active liquid crystals to de-wetting liquid droplets: Using mesoscopic models to understand the physical behaviors of cells and tissues
Host: T. Shendruk
Living cells generate and transmit mechanical forces over diverse time-scales and length-scales to determine the dynamics of cell and tissue shape during both homeostatic and pathological processes, from early embryonic development to cancer metastasis. These forces arise from the cell cytoskeleton, a scaffolding network of entangled protein polymers driven out-of-equilibrium by enzymes that convert chemical energy into mechanical work. However, how molecular interactions within the cytoskeleton lead to the accumulation of mechanical stresses that determine the dynamics of cell shape is unknown. Furthermore, how cellular interactions are subsequently modulated to determine the shape of the tissue is also unclear. To bridge these scales, our group in collaboration with others, uses a combination of experimental, computational and theoretical approaches. On the molecular scale, we use active nematic liquid crystals as a framework to understand how mechanical stresses are produced and transmitted within the cell cytoskeleton. On the scale of cells and tissues, we abstract these stresses to surface tension in a liquid droplet and draw analogies between the dynamics of droplet wetting (and dewetting) and the shape dynamics of cells and simple tissues. Together, we attempt to develop comprehensive description for how cytoskeletal stresses translate to the physical behaviors of cells and tissues with significant phenotypic outcomes such as cancer metastasis.
January 23, 2018: Vladimir Itskov, Pennsylvania State University
Inferring a sensory space from neural responses
Host: T. Shendruk
The brain builds its internal sensory representations based on the structure of neural activity. A number of modalities, such as the early visual system and the spatial map in hippocampus, are relatively well-characterized. However some sensory systems, such as olfaction, remain enigmatic. Perhaps the primary difficulty is that the underlying perceptual space is not well-understood. Can we “build” the sensory space from neural activity alone, without a prior understanding of how the stimuli are organized? I will describe a set of mathematical tools that allow to infer the dimension and the topology of stimulus space and illustrate their utility for two neural systems: hippocampus and early olfaction.
January 30, 2018: Michael D Graham, University of Wisconsin-Madison
Theory of margination in blood and other multicomponent suspensions
Host: T. Shendruk
Blood is a suspension of objects of various shapes, sizes and mechanical properties, whose distribution during flow is important in many contexts. Red blood cells tend to migrate toward the center of a blood vessel, leaving a cell-free layer at the vessel wall, while white blood cells and platelets are preferentially found near the walls, a phenomenon called margination that is critical for the physiological responses of inflammation and hemostasis. Additionally, drug delivery particles in the bloodstream also undergo margination – the influence of these phenomena on the efficacy of such particles is unknown.
In this talk a mechanistic theory is developed to describe segregation in blood and other confined multicomponent suspensions. It incorporates the two key phenomena arising in these systems at low Reynolds number: hydrodynamic pair collisions and wall-induced migration. The theory predicts that the cell-free layer thickness follows a master curve relating it in a specific way to confinement ratio and volume fraction. Results from experiments and detailed simulations with different parameters (flexibility of different components in the suspension, viscosity ratio, confinement, among others) collapse onto the same curve. In simple shear flow, several regimes of segregation arise, depending on the value of a “margination parameter” M. Most importantly, there is a critical value of M below which a sharp “drainage transition” occurs: one component is completely depleted from the bulk flow to the vicinity of the walls. Direct simulations also exhibit this transition as the size or flexibility ratio of the components changes. Results are presented for both Couette and plane Poiseuille flow. Experiments performed in the laboratory of Wilbur Lam indicate the physiological and clinical importance of these observations.
February 1, 2018 (2 PM) Archishman Raju, Cornell University
Information geometry and the renormalization group
Host: E. Siggia
Microscopically diverse systems often yield to surprisingly simple effective theories. The renormalization group (RG) describes how system parameters change as the scale of observation grows and gives a precise explanation for this emergent simplicity in physics. We use information geometry to reformulate the RG as a statement about how the distinguishability of microscopic parameters depends on the scale of observation. We show that information about relevant parameters is preserved, with distances along relevant directions maintained under flow. By contrast, irrelevant parameters become less distinguishable under the flow, with distances along irrelevant directions contracting. We apply our tools to understand the emergence of the diffusion equation and more general statistical systems described by a free energy. This suggests a way to identify relevant directions for more general coarsening procedures.
February 6, 2018 (2 PM) Shou-Wen Wang, Princeton University
Adaptation unifies emergent oscillations in quorum sensing populations
Host: E. Siggia
Adaptation is a ubiquitous trait of living organisms in dealing with the outside world. At the cellular level, high sensitivity over a broad range of environmental conditions is achieved via biomolecular circuits that operate out of thermal equilibrium. Here we report an unexpected link between sensory adaptation and auto-induced collective oscillations in dense cell populations. We uncover a frequency regime where adaptive cells amplify temporal variations of the extracellular signal. Deeply rooted in nonequilibrium thermodynamics, the design principle unites several known examples of dynamical quorum sensing, and provides a new perspective on regulatory mechanisms behind glycolytic oscillations in yeast cell suspensions.
February 13, 2018
TBA
Host: T. Shendruk
February 20, 2018: (2 PM) Alexander Mietke, MPI Physics of Complex Systems
Self-organization of curved and deforming active surfaces
Host: E. Siggia
Mechano-chemical processes in thin biological structures, such as the cellular cortex or epithelial sheets, play a key role during the morphogenesis of cells and tissues. Emergent dynamics in these processes can arise from a feedback loop in which active mechanical forces, by inducing material flows, indirectly affect their own chemical regulation. It has been demonstrated in simple, fixed geometries that this mechanism enables self-organized patterning, but the interplay of mechano-chemical processes with complex surface geometries and shape changes of the material remains to be explored. In this work, we employ the theory of active surfaces and develop analytical and numerical tools to study these materials in curved and dynamically evolving geometries. Additionally, diffusive and advective transport processes can redistribute molecules responsible for local stress generation within the surface, which resembles the interplay between active forces, the shape changes they imply and the effects this has on their regulation. Within this framework, cell polarization, contractile ring formation before cytokinesis, as well as pulsatile dynamics in active tubular structures can be understood as natural emergent phenomena. Our approach provides novel opportunities to explore different scenarios of mechano-chemical self-organization and can help understand the role of shape as an effective regulatory element in morphogenetic processes.
February 27, 2018: Nikolaus Kriegeskorte, Columbia University
Building deep neural network models to understand biological vision
Host: C. Kirst
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. This brain-inspired technology provides the basis for tomorrow’s computational neuroscience [1]. Deep convolutional neural nets trained for visual object recognition have internal representational spaces remarkably similar to those of the human and monkey ventral visual pathway [2]. Functional imaging and invasive neuronal recording provide increasingly rich measurements of brain activity in humans and animals, but a challenge is to leverage such data to gain insight into the brain’s computational mechanisms [3, 4].
In my lab, we build neural network models of primate vision, inspired by biology and guided by engineering considerations [5]. We also develop statistical inference techniques that enable us to adjudicate between complex brain-computational models on the basis of brain and behavioral data [3, 4]. I will discuss recent work extending deep convolutional feedforward vision models by adding recurrent signal flow and stochasticity, two characteristics of biological neural networks. This improves inferential performance and enables neural networks to more accurately represent their own uncertainty.
[1] Kriegeskorte N (2015) Annu. Rev. Vis. Sci. 2015. 1:417-46.
[2] Khaligh-Razavi SM, N Kriegeskorte (2014) PLoS Computational Biology
[3] Diedrichsen J, Kriegeskorte N (2017) PLoS Computational Biology
[4] Kriegeskorte N, Diedrichsen (2016) J Phil. Trans. R. Soc. B.
[5] Spoerer CJ, McClure P, Kriegeskorte N (2017) Frontiers.
March 6, 2018
TBA
Host: D. Zeevi
March 13, 2018: Maxime Deforet, Laboratoire Jean Perrin UPMC
To move, or not to move, that is the evolutionary question: Evolution of growth and dispersal in bacterial populations
Host: T. Shendruk
I will present two projects demonstrating the interplay between growth and dispersal in evolutionary contexts. In the first one, I focus on the evolution of expanding populations, such as bacterial colonies or cancerous tumors. Population expansion is driven by growth and dispersal.
The evolutionary fate of a mutant also depends on both growth and dispersal. For instance, when could moving faster and growing slower be a favorable strategy? Starting with the Fisher-KPP equation, I come up with a quantitative rule of invasion, which depends on growth and dispersal rates of the ancestor and the mutant. Theoretical findings are supported by experiments with bacterial swarming colonies, as well as data from ecology literature.
In the second project, I address the question of design optimization of a biochemical network (the C-di-GMP network in bacteria) that integrates signals from the environment and regulates genes for growth and dispersal. This network needs to be trained to provide the optimal outcome when the environment alternatively favors growth or dispersal. I show that natural selection in this network architecture is mathematically equivalent to training a machine learning model to solve a classification problem.
The evolutionary fate of a mutant also depends on both growth and dispersal. For instance, when could moving faster and growing slower be a favorable strategy? Starting with the Fisher-KPP equation, I come up with a quantitative rule of invasion, which depends on growth and dispersal rates of the ancestor and the mutant. Theoretical findings are supported by experiments with bacterial swarming colonies, as well as data from ecology literature.
In the second project, I address the question of design optimization of a biochemical network (the C-di-GMP network in bacteria) that integrates signals from the environment and regulates genes for growth and dispersal. This network needs to be trained to provide the optimal outcome when the environment alternatively favors growth or dispersal. I show that natural selection in this network architecture is mathematically equivalent to training a machine learning model to solve a classification problem.
March 20, 2018: Madhav Mani, Northwestern University
Drosophila eye development: A physical view
Host: T. Shendruk
Developmental biology is at a special point in its history: 1) Model organisms have been extensively characterized, 2) the molecular parts list has largely been enumerated, and 3) live-imaging and sequencing tools have matured. What remains is to understand how the system works. I will present work in progress in collaboration with Richard Carthew that focuses on a diverse set of phenomena made manifest during Drosophila eye development. Incomplete results related to collective cell-fate determination, collective polarization, and mechanics will be presented. An emphasis will be placed on the importance of taking a dynamical view of development, and mathematical modeling as a way to synthesize disparate observations, guide experimentation, and make predictions.
March 27, 2018: Sharon Swartz, Brown University
Evolution and motor control in bat flight – A wing and a prayer?
Host: T. Shendruk
Bat wings evolved from grasping, manipulating mammalian hands, and this origin influences the biomechanics of flight in bats in comparison to flight in birds and insects. Therefore, an evolutionary perspective is critical to advancing the comparative biology of flight, and helps distinguish those aspects of flight that are shared in all flying animals and those features that are unique to bats. Low weight, particularly in the wings, is important for all flying animals, but selection for reduced wing mass in bats must interact with aspects of neural control in the most morphologically complex of animal wings. In addition, the nature of wing skin as a complex functional material and the capacity to modulate wing mechanical properties during flight by an unusual group of muscles found only in bats proves critical to bat flight performance. Improved understanding of the functional architecture of bat wings not only provides insight into steady-state flight behaviors, but also holds promise for solving problems concerning bats’ abilities to recover from perturbations, fly effectively even following wing damage or injury, etc. This approach requires sophisticated bioengineering techniques such as particle image velocimetry, multi-camera high speed videography, and dynamic modeling, but also low-tech methods including polarized light photography, histology, and anatomical description.
April 3, 2018: Lakshminarayanan Mahadevan, Harvard
Wissdom of hives and mounds: Collective problem solving by super-organisms
Host: T. Shendruk
Social insects are capable of solving complex physiological problems using collective strategies. I will discuss our work on some of these problems that include the physiology and morphogenesis of termite mounds, and active mechanisms for ventilation, mechanical adaptation and thermoregulation in bee aggregates.
April 10, 2018: Julie Theriot, Stanford
The Fast and the Furious: Mechanics and dynamics of rapid cell motility
Host: T. Shendruk
Directed crawling motility of animal cell types ranging from neurons to macrophages requires the coordinated force-generating activity of multiple mechanical elements. Much molecular detail is now known about the constituents of some mechanical submachines such as the polymerizing actin network and the adhesion complexes, but it is not yet clear how these elements all work together to generate coherent, directed motion at the level of the whole cell. In order to understand cellular mechanisms of large-scale coordination, our work focuses on two extremely fast-moving cell types, the fish epidermal basal keratocyte responsible for the rapid closure of wounds in fish skin, and the human neutrophil that hunts down and kills microbial invaders. Despite their very different biological roles and apparent behaviors, these cell share fundamental biophysical mechanisms of self-organization and movement coordination.
April 17, 2018: Ehud Meron, Ben-Gurion University
From patterns to function in dryland ecosystems
Host: T. Shendruk
Dryland landscapes show a variety of vegetation pattern-formation phenomena, two striking examples of which are banded vegetation on hill slopes and nearly hexagonal patterns of bare-soil gaps in grasslands (“fairy circles”). Vegetation pattern formation is a population-level mechanism to cope with water stress, that is coupled to other response mechanisms operating at lower and higher organization levels, such as phenotypic changes at the organism level and biodiversity changes at the community level. Uncovering the roles that vegetation pattern formation plays in the functioning of dryland ecosystems is a challenging problem of particular significance in the current era of climate change and massive human intervention in natural ecosystems. In this talk I will present a platform of mathematical models for dryland ecosystems and use it to study (i) mechanisms of vegetation pattern formation, (ii) the variety of extended and localized patterns that can appear along the rainfall gradient, (iii) the impact of pattern formation on ecosystem response to droughts, and (iv) forms of high-integrity human intervention that do not impair ecosystem function. Universal aspects that might be applicable to other living systems will be emphasized.
April 24, 2018: Leslie Greengard, Flatiron Institute and Courant Institute
Second Annual Peter H. Sellers Lecture
Carson Family Auditorium, CRC, B-Level
The mathematics of biomedical and biophysical imaging
Hosts: M. Magnasco and S. Strickland
Over the last few decades, new mathematical techniques have played an important role in a variety of biomedical and biophysical imaging modalities. Following a brief survey of the field, we will focus on recent progress in nonlinear optimization that is bringing large-scale problems in acoustic scattering and cryo-electron microscopy within practical reach.
May 1, 2018: Allon Klein, Harvard
Tracing lineage and cell differentiation at single cell resolution
Host: T. Shendruk
I will present our efforts to map cellular differentiation hierarchies in zebrafish and xenopus embryos, with further examples in mammalian tissues. A few years ago we (and others) developed droplet-microfluidic technology for single cell RNA-Seq (inDrop), opening up the possibility to infer cell states in an unbiased manner. We recently carried out single-cell RNA sequencing of >200,000 cells from time series of the first 24 hours of life, in two vertebrate species: zebrafish (Danio rerio) and the frog (Xenopus tropicalis). We reconstructed the first tens of cell fate choices in the embryo from axis patterning, germ layer formation, and early organogenesis, and compared the two organisms in their differentiation dynamics. We tested how clonally-related cells traverse these fate choices by developing a transposon-based barcoding approach (“TracerSeq”) for reconstructing single-cell lineage histories. Through different examples, I will try to show some of the challenges and opportunities of single cell RNA-Seq approaches: finding and validating new cell types, identifying new growth factor regulators of fate choice, clarifying points of fate commitment of tissues, but also showing where single cell RNA-Seq data can be misleading.
May 3, 2018: Paul François, McGill
Untangling the biological hairball: Network evolution and fitness based reduction
Host: E. Siggia
Complex mathematical models of interaction networks are routinely used for prediction in systems biology. However, it is difficult to reconcile network complexities with a formal understanding of their behavior. I will introduce several models of immune recognition by T cells and will show how a simple procedure can be used to reduce them to functional submodules, using statistical mechanics of complex systems combined with a fitness-based approach inspired by in silico evolution. Our procedure works by putting parameters or combination of parameters to some asymptotic limit, while keeping (or slightly improving) the model performance, and requires parameter symmetry breaking for more complex models. An intractable model of immune recognition with close to a hundred individual transition rates is reduced to a simple two-parameter model, and connected to the “adaptive sorting” principle that we previously identified and experimentally validated. Our procedure extracts three different mechanisms for early immune recognition, and automatically discovers similar functional modules in different models of the same process allowing for model classification and comparison.
May 8, 2018: Jeffrey Guasto, Tufts University
Transport, topology, and taxis: Bacterial motility in porous media flows
Host: T. Shendruk
Swimming cells, including bacteria, sperm, and plankton, play integral roles in processes ranging from human reproduction to ecosystem dynamics to bioremediation in soil. In particular, swimming cells ubiquitously live in dynamic fluid environments that are characterized by complex porous microstructure. In this talk, we will examine the physical mechanisms underlying the transport of motile bacteria in a model microfluidic porous medium, which is used to precisely control both microstructure and flow. We show that such confined flows generate striking heterogeneity in the spatial distribution of motile bacteria due to interactions between cell shape and fluid flow. Unlike passive Brownian particles, the effective diffusion of actively swimming ‘run-and-tumble’ bacteria is significantly hindered in flow. In the case of magnetotactic bacteria – which use Earth’s magnetic field for navigation – we demonstrate that porous media flows can entirely halt their directed motility, locally trapping cells in the porous microstructure. This work illustrates how the physical environment impacts fundamental survival strategies of swimming cells and carries potential implications for biofilm formation and resource competition biomes.
May 15, 2018: Martin Jonikas, Princeton
Systems and Synthetic Biology of Photosynthetic Organisms
Host: D. Zeevi
Approximately one-third of global carbon-fixation occurs in an overlooked algal organelle called the pyrenoid. The pyrenoid contains the CO2-fixing enzyme Rubisco, and enhances carbon-fixation by supplying Rubisco with a high concentration of CO2. Since the discovery of the pyrenoid over 130 years ago, the molecular structure and biogenesis of this ecologically fundamental organelle have remained enigmatic. To advance our understanding of the pyrenoid and of photosynthetic organisms more broadly, we have developed new tools for the unicellular model alga Chlamydomonas reinhardtii. These tools include the world’s first genome-wide collection of mapped mutants in any single-celled photosynthetic organism, as well as methods for high-throughput localization of proteins and identification of protein-protein interactions. By applying these tools, we have increased the number of known pyrenoid components from 6 to over 80, and discovered the existence of three new protein layers in the pyrenoid- a plate-like layer, a mesh layer, and a punctate layer. We discovered that an abundant pyrenoid protein, Essential Pyrenoid Component 1 (EPYC1), works as a molecular glue that binds Rubisco holoenzymes together to form the matrix at the core of the pyrenoid. Furthermore, contrary to longstanding belief that the pyrenoid matrix is a solid structure, we have discovered that the matrix behaves as a liquid droplet, which mixes internally, divides by fission, and dissolves and condenses during the cell cycle. Our efforts have provided fundamental insights into pyrenoid protein composition, structural organization and biogenesis. Working with our collaborators in the Combining Algal and Plant Photosynthesis project, we aim to transfer algal pyrenoid components into higher plants to enhance carbon fixation and yields in crops.
May 22, 2018: Alexandra Zidovska, CSMR NYU
The “self-stirred” genome: Bulk and surface dynamics of the chromatin globule
Host: T. Shendruk
Chromatin structure and dynamics control all aspects of DNA biology yet are poorly understood. In interphase, time between two cell divisions, chromatin fills the cell nucleus in its minimally condensed polymeric state. Chromatin serves as substrate to a number of biological processes, e.g. gene expression and DNA replication, which require it to become locally restructured. These are energy-consuming processes giving rise to non-equilibrium dynamics. Chromatin dynamics has been traditionally studied by imaging of fluorescently labeled nuclear proteins and single DNA-sites, thus focusing only on a small number of tracer particles. Recently, we developed an approach, displacement correlation spectroscopy (DCS) based on time-resolved image correlation analysis, to map chromatin dynamics simultaneously across the whole nucleus in cultured human cells [1]. DCS revealed that chromatin movement was coherent across large regions (4–5μm) for several seconds. Regions of coherent motion extended beyond the boundaries of single-chromosome territories, suggesting elastic coupling of motion over length scales much larger than those of genes [1]. These large-scale, coupled motions were ATP-dependent and unidirectional for several seconds. Following these observations, we developed a hydrodynamic theory [2] as well as a microscopic model [3] of active chromatin dynamics. In this work we investigate the chromatin interactions with the nuclear envelope and compare the surface dynamics of the chromatin globule with its bulk dynamics [4].
[1] Zidovska A, Weitz DA, Mitchison TJ, PNAS, 110 (39), 15555-15560, 2013
[2] Bruinsma R, Grosberg AY, Rabin Y, Zidovska A, Biophys. J., 106 (9), 1871-1881, 2014
[3] Saintillan D, Shelley MJ, Zidovska A, https://www.biorxiv.org/content/early/2018/05/10/319756, 2018
[4] Chu F, Haley SC, Zidovska A, PNAS, 114 (39), 10338-10343, 2017
[1] Zidovska A, Weitz DA, Mitchison TJ, PNAS, 110 (39), 15555-15560, 2013
[2] Bruinsma R, Grosberg AY, Rabin Y, Zidovska A, Biophys. J., 106 (9), 1871-1881, 2014
[3] Saintillan D, Shelley MJ, Zidovska A, https://www.biorxiv.org/content/early/2018/05/10/319756, 2018
[4] Chu F, Haley SC, Zidovska A, PNAS, 114 (39), 10338-10343, 2017
June 26, 2018: Mladen Barbic, HHMI
Possible Magneto-Thermal and Magneto-Mechanical Mechanisms of Ion Channel Activation in Magneto-Genetics
Hosts: A. Vaziri and J. Friedman
The palette of tools for stimulation and regulation of neural activity is continually expanding. One of the new methods being introduced is Magneto-Genetics, where thermo-sensitive and mechano-sensitive ion channels are genetically engineered to be closely coupled to the iron-storage protein ferritin. Such genetic constructs provide a powerful new way of non-invasively activating ion channels in-vivo using external magnetic fields that easily penetrate biological tissue. Initial reports that introduced this new technology have sparked a vigorous debate on the plausibility of physical mechanisms of ion channel activation by means of external magnetic fields. I will argue that the initial criticisms leveled against Magneto-Genetics as being physically implausible were based on the overly simplistic and unnecessarily pessimistic assumptions about the magnetic spin configurations of iron in ferritin protein, and did not comprehensively consider all possible magnetic-field-based mechanisms of ion channel activation in Magneto-Genetics. I will present and propose several new magneto-thermal and magneto-mechanical mechanisms of ion channel activation by iron-loaded ferritin protein, and suggest experiments on a single iron particle-ferritin protein level that may elucidate and clarify some of the mysteries that presently challenge our understanding of the reported biological experiments.
September 18, 2018: Christina Kurzthaler, University of Innsbruck
Spatiotemporal dynamics of active agents and the buckling behavior of a semiflexible polymer
Host: D. Zeevi
Various challenges are faced when microorganisms or artificial self-propelled particles move autonomously in aqueous media at low Reynolds number. These active agents are intrinsically out of equilibrium and exhibit peculiar dynamical behavior due to the complex interplay of directed swimming motion and stochastic fluctuations. An intriguing feature displayed by microswimmers is the randomization of their swimming motion at large length scales due to reorientation mechanisms such as rotational diffusion or instantaneous tumbling typical of flagellated bacteria. In this talk, I will present recent theoretical advances in the analysis of the spatiotemporal dynamics of different types of active agents in terms of the experimentally measurable intermediate scattering function. Our analytical predictions fully characterize experimental observations of catalytic Janus particles, a paradigmatic class of synthetic active agents, from the smallest length scales where translational Brownian motion dominates, up to the largest ones, which probe the randomization of the swimming direction due to rotational diffusion. Moreover, we elucidate the run-and-tumble motion of E. coli bacteria in terms of a renewal theory. I will also show that our theoretical framework finds application in polymer physics and present our results on the elastic behavior of a semiflexible polymer under compression.
September 25, 2018: Ophir Shalem, UPenn
CRISPR based functional genomics for studying the cell biology of neurodegenerative diseases
Host: D. Zeevi
The CRISPR associated Cas9 nuclease has emerged as an exciting new tool for genome-wide pooled forward genetic screens. I will describe our labs efforts to adapt such approaches to study protein quality control pathways and neurodegenerative disease. Specifically, the development of sensitive fluorescence readouts to follow aberrant protein processing, expression, stability and aggregation associated with many of those diseases and combining it with marker-based pooled CRISPR screens for therapeutic target discovery.
October 2, 2018: Lera Boroditsky, UCSD
How the languages we speak shape the ways we think
Host: D. Zeevi
This seminar will take place at the Carson Family Auditorium (CRC)
This seminar will take place at the Carson Family Auditorium (CRC)
Do people who speak different languages think differently? Do languages merely express thoughts, or do they secretly shape the very thoughts we wish to express? Are some thoughts unthinkable without language? Why do we think the way we do? Why does the world appear to us the way it does? Humans communicate with one another using 7,000 or so different languages, and each language differs from the next in innumerable ways. At stake are basic questions all of us have about ourselves, human nature, and reality. I will discuss research conducted around the world and focus on how language shapes the way we think about color, space, time, causality, and agency.
October 9, 2018: Tami Lieberman, MIT – CANCELLED
De novo mutations in human microbiomes
Host: D. Zeevi
There is an enormous potential for evolution within each of our microbiomes, with billions of new mutations being created each day. In this talk, I will highlight the power of tracking within-person evolution for understanding bacterial transmission, identifying genes critical to long-term survival, and for understanding evolutionary principles. I will present examples from infectious diseases, the gut microbiome, and the skin microbiome. Relevant DOIs: 10.1128/mSystems.00171-17, 10.1101/208009, 10.1038/nm.4205.
October 18, 2018: Partha Mitra, CSHL
The Study of Intelligent Machines
Host: D. Zeevi
The talk will be in two parts. The first part will summarize ongoing work on generating a brain-wide map of brain circuits in the Mouse and the Marmoset at a mesoscopic scale, defined as the transitional scale from individual-variation dominated to a species-typical pattern. The second part will outline some theoretical work on machine learning, and in particular address the puzzling observation that highly overparametrized models seem to still offer good generalization performance. The possibility of a robust crosstalk between an empirical and theoretical research program on intelligent machines will be discussed.
October 23, 2018: Dmitri Petrov, Stanford
Barcoding Rapid Evolution
Host: D. Zeevi
Adaptation is the central evolutionary process and is at the core of some of the greatest challenges facing humanity. Infectious disease, cancer, evolution of resistance to drugs and pesticides are all problems of evolutionary adaptation. Until recently, adaptation was thought to be so idiosyncratic and infrequent that it seemed impossible to obtain enough empirical data about adaptive evolution to investigate it in a systematic way. However, we now know that adaptation is at times sufficiently rapid, recurrent, and involves mutations of such large effect that adaptive dynamics can be quantified on short time scales and with powerful statistical replication. Generally rapid adaptation is a feature of large populations, where the dynamics are not limited by the waiting time for adaptive mutations such that multiple adaptive clones increase in frequency simultaneously, making it difficult to study their individual behaviors. I will describe how we use barcoding and genetic engineering to uncover the dynamics of adaptation in such large populations using experimental evolution in large populations of yeast and in the mouse model of lung cancer.
October 30, 2018: Natalie Jeremijenko, NYU Steinhardt
Doctors without Disciplinary Borders, a recruitment discussion
Host: D. Zeevi
November 13, 2018: Daniel Segré, Boston University
Ecosystem-level metabolic networks
Host: D. Zeevi
Metabolism, in addition to being the “engine” of every living cell, plays a major role in the cell-cell and cell-environment relations that shape the dynamics and evolution of microbial communities, e.g. by mediating competition and cross-feeding interactions between different species. Despite the increasing availability of metagenomic sequencing data for numerous microbial ecosystems, fundamental aspects of these communities, such the maintenance of diversity, the unculturability of many isolates, and the conditions necessary for taxonomic or functional stability, are still poorly understood. In our lab, we develop and test mechanistic computational models for the dynamics and evolution of interactions between different organisms based on the knowledge of their entire metabolic networks, with applications in the study of natural and synthetic microbial communities.
November 27, 2018: Jan Drugowitsch, Harvard
The unreasonable effectiveness of diffusion models in decision neuroscience
Host: D. Zeevi
Diffusion models, a popular family of decision-making models, are despite their simplicity able to well-describe human and animal decision-making behavior across a surprisingly wide range of different tasks. I will analyze their versatility from a normative perspective, by asking if the behavior they describe is optimal in some sense. This turns out to be the case for a wide range of different tasks that rely on perceptual evidence, and even for decisions that require subjective value judgments, justifying the benefits of acquiring such a decision strategy. Furthermore, it demonstrates that the studied tasks imposed diffusion model-like behavior that might disappear once these tasks become more complex.
December 4, 2018: Grégoire Altan-Bonnet, NIH CCR
T cell decision making in the immune system: from low to high dimension
Host: D. Zeevi
Recent progress in systems immunology have ushered a quantitative understanding of T cells’ ability to discriminate antigens (e.g. self vs non-self) on a short timescales (< 1 hr). Yet modeling how immune responses unfold over long timescales (>1 week) remains a challenge with fundamental and clinical applications. Here we will discuss how cell-to-cell communications via cytokine exchange constitute a solution to bridge short and long timescales, local and global responses towards achieving accurate antigen discrimination at the system level. We will present our experimental platform (using CyTOF mass cytometry and a dedicated robotic platform ) to deliver multiplexed time-resolved “high-dimensional” measurements of T cell activation. We will show how we classify complex patterns of T cell activation with machine learning algorithms (top-to-bottom approaches). In parallel, we will discuss how we use biochemical modeling (bottom-up approach) to dissect how signaling cross-talks, synergism and antagonism (between antigen and cytokine response) enable scaling, convergence and divergence of T cell activation.
December 11, 2018, 2pm: Gautam Reddy Nallamalla, UCSD
Learning to soar like a bird using atmospheric thermals
Host: E. Siggia
Soaring birds often rely on ascending thermal plumes (thermals) in the atmosphere as they search for prey or migrate across large distances. How soaring birds find and navigate thermals is unknown. We used reinforcement learning to train gliders to navigate simulated convective turbulent flows. Lessons from simulations allowed us to teach a glider to navigate atmospheric thermals autonomously in the field. Gliders of two-meter wingspan were equipped with a flight controller that precisely controlled the bank angle and pitch, modulating these at intervals with the aim of gaining as much lift as possible. A navigational strategy was determined solely from the gliders’ pooled experiences collected over several days in the field. The strategy relies on methods to accurately estimate the local vertical wind accelerations and the roll-wise torques on the glider, which serve as navigational cues. We propose vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds and provide a navigational strategy applicable to autonomous soaring vehicles.
February 7, 2017 (2PM – Physics Fellow Candidate): Henrik Ronellenfitsch, University of Pennsylvania
Adaptation, Growth, and Resilience in Biological Distribution Networks
Host: M. Feigenbaum
Highly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. We show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as plant and animal vasculature. In addition, we show how the incorporation of spatially collective fluctuating sources yields a minimal model of realistic reticulation in distribution networks and thus resilience against damage. Distribution networks are shown to exhibit a trade-off between resilience, construction cost, and efficiency, with nature selecting for those phenotypes that lie on the Pareto-efficient front.
February 9, 2017 (2PM – Physics Fellow Candidate): David Zeevi, Weizmann Institute of Science
Can our microbes tell us what to eat?
Host: M. Feigenbaum
The past decades have witnessed a surge in the prevalence of obesity, diabetes and the metabolic syndrome. Many of these disorders are associated with high post-meal blood glucose responses, but common dietary methods for controlling these responses have limited efficacy, mainly due to high interpersonal variability in the response to even the same meal. One of the factors underlying this variability is the gut microbiome: a huge ecosystem of bacteria, archaea, viruses and eukaryotes with vast potential metabolic capacity. In our work we developed new tools for the analysis of the gut microbiome and used these tools, along with blood parameters, dietary habits, anthropometrics and physical activity to accurately predict post-meal blood glucose responses to real-life meals. These predictions were then used to design personalized diets which successfully reduced hyperglycemia. Our results suggest that personalized diets can successfully lower post-meal blood glucose and its grave metabolic consequences.
February 14, 2017 (2PM – Physics Fellow Candidate): Edward Banigan, Northwestern University
Mechanisms of spatiotemporal chromosome positioning
Host: M. Feigenbaum
Spatial localization of chromosomes and their genes is tightly controlled throughout the cell cycle, which ensures proper gene expression during interphase and faithful inheritance of the genome by daughter cells during mitosis. The cellular structures that govern these processes — the nucleus and the mitotic spindle — are complex assemblies of biological components, whose mechanical properties are essential to their functions. The two main mechanical components of the cell nucleus are chromatin and the lamina. Corresponding to these components, my coarse-grained polymer model predicts that the nucleus has two mechanical regimes of force response to physical deformations. This picture is supported by micromanipulation experiments in which isolated nuclei are stretched and perturbed through a variety of cell biological and biochemical perturbations. Altogether, this force response observed in the experiments and explained by the model protects the global organization of genome during interphase. During mitosis, after the nucleus disassembles, however, the microtubule spindle governs chromosome positioning. Building on observations from in vitro experiments, I developed a stochastic model for the collective dynamics of microtubules comprising the spindle. The model revealed a striking mechanism for coordinating force-sensitive filament dynamics — a bistable force-velocity relation. Within this model, I capture known metaphase behaviors such as chromosome oscillations and error correction, and I accurately predict the results of novel experiments in which microtubule dynamics are perturbed via Aurora B kinase. The model thus provides a basic framework for physically understanding regulated metaphase chromosome motions as well as defective motions, which can lead to harmful scenarios, such as aneuploidy. Together, these models show how cellular machinery can robustly control the spatiotemporal positions of chromosomes through structure, geometry, and mechanotransduction.
February 16, 2017 (2PM – Physics Fellow Candidate): Jasmine Nirody, University of California-Berkeley
The flagellum unwound: Torque generation in the bacterial flagellar motor
Host: M. Feigenbaum
Bacteria were among the first life forms on Earth, and are found in all of its corners. In order to survive in a variety of environments, bacterial species have developed a variety of locomotive strategies. The most common of these is flagellated swimming, in which bacteria are propelled by the motion of several long filaments that sprout of the cell body. A flagellar filament is rotated by a molecular machine at its base, the aptly-named bacterial flagellar motor (BFM). The motor’s central role in bacterial motility has made uncovering its operating principles a fundamental challenge in biophysics and microbiology. In this talk, I will present a model for the BFM’s fundamental torque-generation mechanism, and show that model predictions are consistent with experimental observations of motors in various external conditions. I will further discuss recent theoretical and experimental advances in our mechanistic understanding of this nanomachine, and the implications of these advances to several essential biological processes such as bacterial pathogenicity, chemotaxis, and biofilm formation.
February 21, 2017 : Canceled
March 7, 2017: Massimo Vergassola, UC San Diego
Navigating turbulent environments
Host: P. Sulc
Thermal soaring by birds and olfactory searches by insects are biological examples of navigation in the presence of orientation cues that are complex due to the physics of fluids. The two problems also have technological applications, namely for extending the autonomy of flying vehicles/gliders, and for the development of olfactory robots. I shall first review the animal behavior, then present the physics of the orientation cues, and finally discuss the corresponding navigation problems.
CANCELLED: March 14, 2017: Clifford Brangwynne, Princeton University
Measuring the Intracellular Dew Point: Phase Transitions in Cells
Host: P. Sulc
In this talk I will discuss our work showing that phase transitions play an important role in organizing the contents of living cells. We focus on a class of membrane-less RNA and protein rich organelles, known as RNP bodies, which help control the flow of genetic information within cells. The nucleolus is one such nuclear RNP body, which is important for cell growth and size homeostasis. We’ve shown that a phase transition model explains many features of nucleolar assembly, and that the internal subcompartments of the nucleolus arise from multi-phase coexistence, which may have important consequences for sequential RNA processing. I will also discuss our new “Optodroplet” approach, which enables spatiotemporal control of phase transitions within living cells, allowing us to begin quantitatively mapping intracellular phase diagrams. This approach has begun to yield rich insights into the link between intracellular liquids, gels, and the onset of pathological protein aggregation.
March 21, 2017: Julius Lucks, Northwestern University
Uncovering How RNA Molecules ‘Make Decisions’ On the Fly: Towards Understanding and Engineering Cotranscriptional RNA Folding
Host: P. Sulc
RNAs are emerging as a powerful substrate for engineering gene expression and cellular behavior since they are now known to control almost all aspects of gene expression. As with all biomolecules, RNA function is intimately related to its structure, since RNA can adopt structures that selectively modulate gene expression. Central questions in biology and bioengineering then are: How do RNAs fold inside cells?; and How can we engineer these folds to control gene expression? In this talk, I will present our work at the interface of these two questions and share results that are beginning to uncover design principles for understanding natural RNAs and engineering RNAs for an array of applications.
I will start by presenting our work on engineering RNA molecular switches that control transcription. The desire to uncover design principles for engineering these RNAs motivates our development of SHAPE-Seq, a technology that couples chemical probing with next-generation sequencing and that helps characterize RNA structures on an ‘omics’ scale. I will then describe our exciting recent developments in using SHAPE-Seq to help break open one of the frontiers of RNA structure-function relationships by uncovering at nucleotide resolution how RNAs fold cotranscriptionally. Specifically I will highlight new data on uncovering the ligand-dependent folding pathways of riboswitches, and how we are beginning to use these datasets to computationally reconstruct cotranscriptional folding pathways. This new ability is allowing us to ask deep questions about how RNA molecules make regulatory decisions ‘on the fly’ during the dynamic process of transcription. By probing the fundamental processes of RNA folding and function, these studies are expected to greatly aid RNA engineering.
I will start by presenting our work on engineering RNA molecular switches that control transcription. The desire to uncover design principles for engineering these RNAs motivates our development of SHAPE-Seq, a technology that couples chemical probing with next-generation sequencing and that helps characterize RNA structures on an ‘omics’ scale. I will then describe our exciting recent developments in using SHAPE-Seq to help break open one of the frontiers of RNA structure-function relationships by uncovering at nucleotide resolution how RNAs fold cotranscriptionally. Specifically I will highlight new data on uncovering the ligand-dependent folding pathways of riboswitches, and how we are beginning to use these datasets to computationally reconstruct cotranscriptional folding pathways. This new ability is allowing us to ask deep questions about how RNA molecules make regulatory decisions ‘on the fly’ during the dynamic process of transcription. By probing the fundamental processes of RNA folding and function, these studies are expected to greatly aid RNA engineering.
March 28, 2017: Stephen Altschul, National Center for Biotechnology Information
Inaugural Peter H. Sellers Lecture: Dirichlet Mixtures, the Dirichlet Process, and the Topography of Amino Acid Multinomial Space
Host: Marcelo Magnasco
Greenberg Building, B-Level, Carson Family Auditorium
Greenberg Building, B-Level, Carson Family Auditorium
The Dirichlet Process is used to estimate probability distributions that are mixtures of an unknown and unbounded number of components. Amino acid frequencies at homologous positions within related proteins have been fruitfully modeled by Dirichlet mixtures, and we have used the Dirichlet Process to construct such distributions. The resulting mixtures describe multiple alignment data substantially better than do those previously derived. They consist of over 500 components, in contrast to fewer than 40 previously, and provide a novel perspective on protein structure. Individual protein positions should be seen not as falling into one of several categories, but rather as arrayed near probability ridges winding through amino-acid multinomial space.
April 4, 2017: Kunihiko Kaneko, University of Tokyo
Deep Linearity in Adaptation and Evolution: Macroscopic theory, microscopic simulation, and bacterial experiments
Host: T. Shendruk
Quantitative characterization of plasticity, robustness, and evolvability is one of the most important issues in biology. Based on statistical physics and dynamical-systems theory, we present a macroscopic theory of fluctuation and responses in cellular states. By assuming that cells undergo steady growth, protein expression of thousands of genes is shown to change along a one-dimensional manifold in the state space in response to the environmental stress. This leads to a macroscopic law that cellular-state changes satisfy, as is confirmed by adaptation experiments of bacteria under stress. Next, we present proportionality between phenotypic changes by genetic evolution and by environmental adaptation, uncovered both in bacterial experiments and simulations. This relationship is then formulated by the hypothesis that phenotypic changes in adaptation and evolution are dominantly constrained along one-dimensional path. Possible extension of the theory to non-growing cellular states and to multi-level evolution for multicellularity will be briefly discussed.
References
1. Kaneko K., Life: An Introduction to Complex Systems Biology, Springer (2006)
2. K. Sato, Y,Ito, T.Yomo, K. Kaneko, “On the Relation between Fluctuation and Response in Biological Systems” Proc. Nat. Acad. Sci. USA 100 (2003) 14086-14090
3. K. Kaneko, “Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics” PLoS One(2007) 2 e434
4. K. Kaneko, “Phenotypic Plasticity and Robustness: Evolutionary Stability Theory, Gene Expression Dynamics Model, and Laboratory Experiments”, Evolutionary Systems Biology (2012) (Springer, ed. O. Soyer)
5. K. Kaneko, C.Furusawa, T. Yomo, “Macroscopic phenomenology for cells in steady-growth state”, Phys.Rev.X(2015) 011014
7. C. Furusawa, K. Kaneko “Global Relationships in Fluctuation and Response in Adaptive Evolution”, J of Royal Society Interface (2015)
References
1. Kaneko K., Life: An Introduction to Complex Systems Biology, Springer (2006)
2. K. Sato, Y,Ito, T.Yomo, K. Kaneko, “On the Relation between Fluctuation and Response in Biological Systems” Proc. Nat. Acad. Sci. USA 100 (2003) 14086-14090
3. K. Kaneko, “Evolution of Robustness to Noise and Mutation in Gene Expression Dynamics” PLoS One(2007) 2 e434
4. K. Kaneko, “Phenotypic Plasticity and Robustness: Evolutionary Stability Theory, Gene Expression Dynamics Model, and Laboratory Experiments”, Evolutionary Systems Biology (2012) (Springer, ed. O. Soyer)
5. K. Kaneko, C.Furusawa, T. Yomo, “Macroscopic phenomenology for cells in steady-growth state”, Phys.Rev.X(2015) 011014
7. C. Furusawa, K. Kaneko “Global Relationships in Fluctuation and Response in Adaptive Evolution”, J of Royal Society Interface (2015)
April 11, 2017: Dan Landau, Weill Cornell Medical College
On the Evolution of Leukemia
Host: T. Shendruk
Cancer progression, relapse and resistance are the result of an evolutionary optimization process. Vast intra-tumoral diversity provides the critical substrate for cancer to evolve and adapt to the selective pressures provided by effective therapy. Thus, understanding intra-tumoral diversity and evolutionary dynamics will be a critical step in the development of effective, curative therapies for cancer.
In order to study these questions, we characterized the intra-tumoral genetic heterogeneity of chronic lymphocytic leukemia (CLL) using massively parallel sequencing of large patient cohorts (Landau et al, Cell, 2013, Nature, 2015). These studies have shown that CLLs contain genetically distinct subpopulations that compete and mold the genetic makeup of the malignancy. Furthermore, we have demonstrated that this heterogeneity can help predict the future evolutionary trajectories of CLL, and that higher intra-tumoral heterogeneity in the pre-treatment sample predicts adverse outcome.
Ongoing efforts are dedicated to studying the quantitative evolutionary dynamics that enable the relapse clone to replace the pre-treatment clone (Burger et al, Nature Communications, 2016, and manuscript in review). Using deep sequencing with high temporal resolution we determine the therapy specific clonal fitness with first line chemoimmunotherapy and targeted therapy. These investigations offer a novel framework for the study of the evolutionary dynamics that underlie disease relapse, directly in patients. Moreover, clonal dynamics provide precision prognostication of the timing and clonal composition of relapse. We also use these measurements to tailor personalized therapeutic regimens that would result in longer remissions, laying the foundations for algorithmically driven cancer therapy.
Additionally, in order to comprehensively study cancer evolution, we developed tools to study intra-tumoral epigenetic heterogeneity, as epigenetic somatic changes are heritable and impact the cellular fitness that is selected in the evolutionary process. With these tools, we uncovered a central feature of the cancer epigenome: massive stochastic disorder in methylation patterns. We have further shown that this stochastic disorder impacts transcription, evolution and clinical outcome (Landau et al, Cancer Cell, 2014). Thus, methylation changes in cancer may be similar to the process of genetic diversification, in which stochastic trial and error leads to rare fitness enhancing events. Furthermore, we have performed large-scale single-cell bisulfite sequencing of CLL cells and B cells from healthy donors. We found a close relationship between epimutation and the evolutionary age of the cells. As each generation has a given likelyhood of generating additional stochatic DNA methylation changes, stochastic disorder estimate may reflect the number of generations in the cells evolutionary history. Finally, the phased single cell data allows to reconstruct phylo(epi)genetic relationship between the cells, and infer the stochastic epimutation rate across the genome.
Collectively, these studies demonstrate the tremendous degree of intra-tumoral diversity that fuels cancer evolution, and highlight the need to integrate intra-tumoral heterogeneity in the development of the next generation of cancer therapeutics.
In order to study these questions, we characterized the intra-tumoral genetic heterogeneity of chronic lymphocytic leukemia (CLL) using massively parallel sequencing of large patient cohorts (Landau et al, Cell, 2013, Nature, 2015). These studies have shown that CLLs contain genetically distinct subpopulations that compete and mold the genetic makeup of the malignancy. Furthermore, we have demonstrated that this heterogeneity can help predict the future evolutionary trajectories of CLL, and that higher intra-tumoral heterogeneity in the pre-treatment sample predicts adverse outcome.
Ongoing efforts are dedicated to studying the quantitative evolutionary dynamics that enable the relapse clone to replace the pre-treatment clone (Burger et al, Nature Communications, 2016, and manuscript in review). Using deep sequencing with high temporal resolution we determine the therapy specific clonal fitness with first line chemoimmunotherapy and targeted therapy. These investigations offer a novel framework for the study of the evolutionary dynamics that underlie disease relapse, directly in patients. Moreover, clonal dynamics provide precision prognostication of the timing and clonal composition of relapse. We also use these measurements to tailor personalized therapeutic regimens that would result in longer remissions, laying the foundations for algorithmically driven cancer therapy.
Additionally, in order to comprehensively study cancer evolution, we developed tools to study intra-tumoral epigenetic heterogeneity, as epigenetic somatic changes are heritable and impact the cellular fitness that is selected in the evolutionary process. With these tools, we uncovered a central feature of the cancer epigenome: massive stochastic disorder in methylation patterns. We have further shown that this stochastic disorder impacts transcription, evolution and clinical outcome (Landau et al, Cancer Cell, 2014). Thus, methylation changes in cancer may be similar to the process of genetic diversification, in which stochastic trial and error leads to rare fitness enhancing events. Furthermore, we have performed large-scale single-cell bisulfite sequencing of CLL cells and B cells from healthy donors. We found a close relationship between epimutation and the evolutionary age of the cells. As each generation has a given likelyhood of generating additional stochatic DNA methylation changes, stochastic disorder estimate may reflect the number of generations in the cells evolutionary history. Finally, the phased single cell data allows to reconstruct phylo(epi)genetic relationship between the cells, and infer the stochastic epimutation rate across the genome.
Collectively, these studies demonstrate the tremendous degree of intra-tumoral diversity that fuels cancer evolution, and highlight the need to integrate intra-tumoral heterogeneity in the development of the next generation of cancer therapeutics.
April 13, 2017 (NOTE Thursday): Sidhartha Goyal, University of Toronto
Statistical mechanics of stem cells
Host: T. Shendruk
Much of complex biology results from interactions among a large number of individually simpler elements. Behavior of large collection of cells from microbes to stem cells are no different. Nonetheless, the population dynamics of heterogeneous populations is only now beginning to attract attention it deserves because we have only just, within in the last decade or so developed experimental tools for tracking heterogeneous populations. In this talk I will describe how theoretical ideas from statistical mechanics are being used to understand behavior of such heterogeneous populations, focusing on two examples. In first, I will present a coarse-grained model of blood regeneration, which provides a framework to understand large variations (~3 orders of magnitude) among contributions from individual stem cells without active competition. In contrast, the second describes how competition plays a central role in understanding dynamics of reprogramming population of somatic cells.
April 18, 2017: Erwin Frey, LMU Munich
Protein Pattern Formation: Rethinking Nonlinear Dynamics
Host: T. Shendruk
Protein pattern formation is essential for spatial organization of many intracellular processes like cell division, flagellum positioning, and chemotaxis. More generally, these systems serve as model systems for self-organization, one of the core principles of life.
We present a rigorous theoretical framework able to generalize and unify pattern formation for quantitative mass conserving reaction-diffusion models. Mass redistribution controls local chemical equilibria. Separation of diffusive mass redistribution on the level of conserved species provides a general mathematical procedure to decompose complex reaction-diffusion systems into effectively distinct functional units, and to reveal the general underlying bifurcation scenarios. We apply this framework to Min protein pattern formation and identify the mechanistic roles of both involved protein species. MinD generates polarity through phase separation, whereas MinE takes the role of a control variable regulating the existence of polarized MinD patterns. Hence, polarization and not oscillations is the generic core dynamics of Min proteins in vivo. This establishes an intrinsic mechanistic link between the Min system and a broad class of intracellular pattern forming systems based on bistability and phase separation (wave-pinning). Oscillations are facilitated by MinE redistribution and can be understood mechanistically as relaxation oscillations of the polarization direction.
We present a rigorous theoretical framework able to generalize and unify pattern formation for quantitative mass conserving reaction-diffusion models. Mass redistribution controls local chemical equilibria. Separation of diffusive mass redistribution on the level of conserved species provides a general mathematical procedure to decompose complex reaction-diffusion systems into effectively distinct functional units, and to reveal the general underlying bifurcation scenarios. We apply this framework to Min protein pattern formation and identify the mechanistic roles of both involved protein species. MinD generates polarity through phase separation, whereas MinE takes the role of a control variable regulating the existence of polarized MinD patterns. Hence, polarization and not oscillations is the generic core dynamics of Min proteins in vivo. This establishes an intrinsic mechanistic link between the Min system and a broad class of intracellular pattern forming systems based on bistability and phase separation (wave-pinning). Oscillations are facilitated by MinE redistribution and can be understood mechanistically as relaxation oscillations of the polarization direction.
April 25, 2017: Nathan Kutz, University of Washington
Data-driven discovery of governing equations in the engineering, physical and biological sciences
Host: T. Shendruk
We demonstrate that the integration of data-driven dynamical systems and machine learning strategies are now capable of extracting governing laws from time-series measurements of physical/biophysical systems. Specifically, we demonstrate that we can use emerging, large-scale time-series data from modern sensors to directly construct, in an adaptive manner, governing equations, even nonlinear dynamics, that best model the system measured using sparsity-promoting techniques. Recent innovations also allow for handling multi-scale physics phenomenon and control protocols in an adaptive and robust way. The overall architecture is equation-free in that the dynamics and control protocols are discovered directly from data acquired from sensors. The theory developed is demonstrated on a number of example problems. Ultimately, the method can be used to construct adaptive controllers which are capable of obtaining and maintaining optimal states while the machine learning and sparse sensing techniques characterize the system itself for rapid state identification and improved optimization.
April 27, 2017 (Thursday): Lawrence Williams, Rutgers University
Transient Symmetry and Self-Similarity in Proteins: A Protein Structure Theory
Host: P. Sulc
This lecture will outline a simple way to understand proteins. Per-residue interaction factors will be introduced and used to describe protein structure and to understand heat sensitive mutants, protein-protein interactions (PPI), protein-small molecule interactions (PSMI), and other phenomena. The per-residue interaction factor is a function of amino acid identity, local structure, and multibody contributions; from it, folding/interaction free energy can be calculated. Despite its simplicity, the method compares remarkably well with all-atom models. Determination of these factors is no more complex than the rules to a common board game. Interaction factor heat maps are an especially convenient way to depict the stabilized core of a protein and how the core leverages exterior hot spots. We will illustrate these and other points with a variety of proteins, including general principles that emerge directly from the model. If time permits, we will examine case studies, such as the BCR-Abl kinase domain, commercial drugs that target this kinase, phosphorylation of the activation loop, the conformational switch, and mutational resistance to inhibitors that target the inactive form, since these and many other features of proteins are readily understood and rationalized by casual inspection with these factors.
May 2, 2017: Carlos Bustamante, UC Berkeley
The Folding Cooperativity of a Protein is Controlled by the Topology of its Polypeptide Chain
Host: P. Sulc
The three-dimensional structures of proteins often show a modular architecture comprised of discrete structural regions or domains. Cooperative communications between these regions is important to catalysis, regulation and efficient folding; lack of coupling has been implicated in the formation of fibrils and other misfiling pathologies. How different structural regions of a protein communicate and contribute to a protein’s overall energetics and folding, however, is still poorly understood. Here we use a single-molecule optical tweezers approach to indue the selective unfolding of particular regions of T4 lysozyme and monitor the effect of other regions not directly acted on by force. We investigate how the topological organization of a proven (the order of structural elements along the sequence) affects the coupling and folding cooperatively between its domains. To probe the status of the regions not directly subjected to force, we determine the free energy changes during mechanical unfolding using Crooks’ fluctuation theorem. We pull on topological variants (circular per mutants) and find that the topological organization of the polypeptide chain critically determine the folding cooperativity between domains and thus what parts of the folding/unfolding landscape are explored. We speculate that proteins may have evolved to select certain topologies that increase coupling between regions to avoid areas of the landscape that lead to kinetic trapping and misfolding.
May 9, 2017: Megan King, Yale School of Medicine
Physical Integration of Chromatin and the Cytoskeleton: Impacts on nuclear mechanics, chromosomes and transcription
Host: T. Shendruk
The nucleus is mechanically integrated into cells and tissues through LINC complexes, which couple the nuclear envelope to the cytoplasmic cytoskeleton. Nuclear stiffness correlates with tissue stiffness in part through regulation of lamin A expression. However, it is not clear if the lamin A polymer itself imparts nuclear stiffness, or if the ability of lamin A to tether DNA, particularly heterochromatin, to the nuclear envelope (NE) plays a role. Yeast, which lack a nuclear lamina, provide a model system in which to study how tethering of chromatin to the NE influences nuclear stiffness; in this model integral inner nuclear membrane (INM) proteins act as the sole NE tethers. Using a quantitative imaging platform to measure 3D nuclear contours in live cells and an in vitro optical tweezers assay, we found that INM protein tethers promote nuclear stiffness but also restrict chromatin flow to prevent long-term changes in nuclear shape in response to cytoskeletal forces. Interestingly, we find that cells lacking the heterochromatin binding protein HP1 have reduced nuclear stiffness in vivo and in vitro, while driving heterochromatin spreading by deleting the histone H3K9 demethylase makes nuclei more resistant to deformation. This work provides a new framework for thinking about the age-old observation that heterochromatin is constitutively associated with the nuclear envelope in most eukaryotes. In mice, disrupting the ability of cytoskeletal forces to be exerted on chromatin in the nucleus through LINC complex ablation leads to altered differentiation in the skin in vivo and isolated keratinocytes in vitro. These data suggest that physical forces from cell-extracellular matrix (ECM) adhesions are transmitted directly onto the nuclear lamina through LINC complexes to regulate cell fate.
May 16, 2017: Francis Corson, Laboratoire de Physique Statistique de l’ENS
Positional Information and Self-Organization: How the fly lays out its sense organs
Host: T. Shendruk
The emergence of spatial patterns in developing multicellular organisms relies on positional cues and cell-cell communication. Drosophila sensory organs have informed a paradigm where these operate in two distinct steps: prepattern factors drive localized proneural activity, then Notch-mediated lateral inhibition singles out neural precursors. Using a combination of experiments and modeling, we show that Notch signaling also organizes the proneural stripes that resolve into rows of sensory bristles on the fly thorax. Patterning, initiated by a gradient of Delta ligand expression, progresses through inhibitory signaling between and within stripes. Thus Notch signaling can support self-organized tissue patterning as a broad prepattern is transduced by cell-cell interactions into a refined arrangement of cellular fates.
May 18, 2017 (NOTE Thursday): Francois Nédélec, EMBL
Cytoskeletal Mechanics: Contractile Actomyosin Networks
Host: T. Shendruk
The cytoskeleton drives many essential processes in vivo, but for this the system of filaments will arrange itself into different overall spatial organizations, e.g., random, branched networks, parallel bundles, antiparallel arrays, etc. A general objective of our research is to understand what makes these architectures adapted to their tasks. In this talk, I will first focus on 2D disorganized actin networks in which the filaments are oriented randomly in all directions, and are connected both by active molecular motors and passive crosslinkers. Systems with these properties have been reconstituted in vitro, and serve as a model of the cortical actomyosin networks that drive morphogenesis in animal tissues. Although the network components and their properties are known, the requirements for contractility are still poorly understood. I will describe a theory that predicts whether an isotropic network will contract, expand, or conserve its dimensions, depending on the properties of the filaments and the elements that connect them. The theory is simple and encompasses mechanisms of contractions previously proposed.
Julio Belmonte, Francois Nedelec and Maria Leptin
Cell Biology and Biophysics, EMBL Heidelberg, Germany
Julio Belmonte, Francois Nedelec and Maria Leptin
Cell Biology and Biophysics, EMBL Heidelberg, Germany
May 23, 2017: Raul Rabadan, Columbia University
TBA
Host: T. Shendruk
(Thursday): May 25, 2017: Clifford Brangwynne, Princeton University
Measuring the Intracellular Dew Point: Phase Transitions in Cells
Host: P. Sulc
In this talk I will discuss our work showing that phase transitions play an important role in organizing the contents of living cells. We focus on a class of membrane-less RNA and protein rich organelles, known as RNP bodies, which help control the flow of genetic information within cells. The nucleolus is one such nuclear RNP body, which is important for cell growth and size homeostasis. We’ve shown that a phase transition model explains many features of nucleolar assembly, and that the internal subcompartments of the nucleolus arise from multi-phase coexistence, which may have important consequences for sequential RNA processing. I will also discuss our new “Optodroplet” approach, which enables spatiotemporal control of phase transitions within living cells, allowing us to begin quantitatively mapping intracellular phase diagrams. This approach has begun to yield rich insights into the link between intracellular liquids, gels, and the onset of pathological protein aggregation.
September 12, 2017: Jun Zhang, Courant Institute
Thermal convection under our feet and beyond
Host: T. Shendruk
Thermal convection is ubiquitous in nature and can be found in our everyday lives. This subject has been studied by scientists and engineers for many decades, for its rich dynamics and vast applications. In this talk, I will first discuss an experiment as a free-moving floating boundary interacts with a fluid, which is heated from under and cooled from above. The top boundary is mobile and thermally opaque (poor conductor), causing the coupled system to oscillate in space and time. The underlying mechanism is similar to what has been powering the geophysical phenomenon of continental drift, as continents interact with the convective mantle of the earth. In the second experiment, a few seemingly impeding partitions or dividers are inserted into a convective fluid, but the heat-flux that passes through are found to be boosted by several times. Theses results are explained and some new directions that extend the classical picture of thermal convection are also discussed.
September 19, 2017: Rong Li, Johns Hopkins University
Cellular asymmetry in aging and proteostasis
Host: T. Shendruk
Asymmetric cell division provides a means for partitioning not only cell fate determinants but also determinants of cellular age. Increasing evidence suggests that stem cells employ such mechanism for continual maintenance of high proliferative potential. The budding yeast provides an outstanding single-cellular eukaryotic model for study asymmetric aging. Each division cycle of yeast produces a new born bud and an aged mother cell. Two types of aging determinants are segregated asymmetrically: beneficial components that slowly deteriorate but are scarcely replenished, and toxic components that accumulate as a result of acute or chronic cellular stress. Different cellular mechanisms have been discovered to govern the partitioning of these aging determinants. In addition, cellular organelles such as mitochondria play previously unknown roles in cellular quality control and aging.
September 26, 2017: Patrick Oakes, University of Rochester
Zyxin mediates mechanochemical feedback to regulate local cell elasticity
Host: T. Shendruk
Cytoskeletal mechanics, in particular the spatial and temporal regulation of force generation and adhesion, regulate a diverse array of physiological processes. While contractility is known to be largely RhoA dependent, the process by which localized biochemical signals are translated into cell-level responses is poorly understood. Here we combine optogenetic control of RhoA signaling, live-cell imaging, and traction force microscopy to investigate the dynamics of actomyosin-based force generation and to measure the local mechanical properties of the cytoskeleton. Local activation of RhoA not only stimulates local recruitment of actin and myosin but also increases traction forces that rapidly propagate across the cell via stress fibers and drive increased actin flow. Surprisingly, this flow reverses direction when local RhoA activation stops. We identify zyxin as a regulator of stress fiber mechanics, as stress fibers are fluid-like and do not exhibit flow reversal in its absence. Using a physical model, we demonstrate that stress fibers behave elastic-like, even at timescales exceeding the turnover of constituent proteins in the cytoskeleton. Such molecular control of actin mechanics likely plays critical roles in regulating morphodynamic events.
October 3, 2017: Marija Vucelja, University Of Virginia
The Mpemba index and anomalous relaxation
Host: T. Shendruk
Mpemba effect is a counter-intuitive relaxation phenomenon that occurs when, upon coupling to a cold thermal reservoir, a system prepared at a hot temperature relaxes to equilibrium faster than a system prepared at an intermediate temperature. Here we study the possibility of the Mpemba effect for a large class of thermal relaxation processes. We show that an enhanced Mpemba effect is possible for some choices of the initial temperatures, decreasing considerably the cooling times, and introduce a topological index that identifies the existence of such enhancement. We estimate the probability of the Mpemba index for a class of systems with random barriers and energies and show that it is non-vanishing.
October 10, 2017: Bill Bement, University of Wisconsin-Madison
The cell cortex as an excitable medium
Host: T. Shendruk
Cytokinesis is the physical division of the cell into two daughter cells. Cytokinesis results from the assembly and closure of a ring of actin filaments (F-actin) and the motor protein, myosin-2 at cell cortex (ie the outermost part of the cell). Formation of this ring is controlled by the small GTPase Rho, which itself is activated in a ring-like pattern at the cortex by signals from the mitotic spindle and which, when active, promotes F-actin and myosin-2 assembly. We find that prior to developing the ring-like concentration of Rho activity, the cortex displays propagating, undamped waves of Rho activity and F-actin assembly. Surprisingly, during wave propagation, while Rho elicits F-actin assembly, F-actin subsequently inactivates Rho. Experimental and modeling results show that waves represent excitable dynamics of a reaction-diffusion system with Rho as the activator and F-actin the inhibitor. Ect2, a protein that activates Rho and is required for cytokinesis, stimulates cortical excitability, while spindle microtubules inhibit cortical excitability, thereby confining the waves to the equatorial cortex. Manipulation of MgcRacGAP, a putative Ect2 activator and Rho inactivator, causes “jumping” Rho waves that, rather than steadily propagating, appear and disappear as they progress through the cell cortex. We conclude that the cell cortex is an excitable medium and that the excitability is carried not by ions and ion channels, as in other biological examples of excitability but rather by the cytoskeleton and its regulators. We further conclude that cortical excitability is harnessed by the cell to direct cytokinesis.
October 17, 2017: Petr Sulc, Arizona State University
Multi-scale modelling of nucleic acids
Host: T. Shendruk
October 24, 2017: Alison Sweeney, University of Pennsylvania
Eye patches: The evolution of novel soft matter
Host: T. Shendruk
Life on Earth constitutes the most sophisticated iterations in the known universe of what physicists classify as soft matter. Research in my group focuses on learning the physical rules of soft matter self-assembly phenomena via the evolutionary processes by which they arose over Earth’s history. In this view of life as soft matter, evolution, with its own formal rules and algorithms, governs the appearance and diversification of novel forms of soft matter. The field of soft matter was until very recently restricted to analytical consideration of simpler systems like isotropically interacting colloids and cross-linked polymers such as rubber. Our approach allows us to understand soft materials in a nuanced manner that would be inaccessible from more top-down analytical approaches. In this colloquium, I will present the most detailed test case of this perspective to date: the evolutionary appearance of spherical, gradient-index lenses in squids. This complex optical material, first described in theory by Maxwell in 1854, emerges from 5-nm spheroidal proteins via patchy colloidal physics. The lens requires stable, transparent materials throughout the span of packing fractions (from near zero to near one); accordingly, the lens proteins exploit the entire patchy colloidal phase space, and our work is the first demonstration of many of these colloidal organizations in nature. The self-assembling squid proteins exhibit structural nuances that have also been predicted by self-assembly theories, such that the evolved system may provide helpful insight to engineers designing systems at similar lengthscales. Conceptually related projects such as the structure and function of quasi-ordered optics for camouflage of midwater squid eyes will also be discussed.
October 26, 2017 (NOTE Thursday at 4PM): Yitzhak Rabin, Bar-Ilan University
Dynamics of active chromatin
Host: M. Feigenbaum
We discuss some recent experiments and theoretical ideas about chromatin dynamics in live cells and show that understanding these experiments requires the introduction of new concepts such as active noise and scale-dependent viscosity. While the statistical properties of this active, ATP-dependent noise are unknown at present, we argue that anomalous results on dynamics of labelled telomeres in lamin-A-depleted nuclei suggest that this active noise is correlated over very long time scales.
October 31, 2017: Katharina Ribbeck, MIT
Crossing Barriers – Mechanistic Insights from Nature’s Hydrogels
Host: T. Shendruk
The goal of our research is to elucidate the mechanisms that govern selective filtering by mucus, an important biological hydrogel which coats wet surfaces in the body of all animals. Mucus has critical, but poorly understood, biological functions in protecting tissues from attack by pathogens, and facilitating transport of particulate material. I will present our work on basic mechanisms by which mucus barriers exclude, or allow passage of different molecules and pathogens, and the mechanisms pathogens have evolved to penetrate mucus barriers. We hope to provide the foundation for a theoretical framework that captures general principles governing selectivity in mucus, and likely other biological hydrogels such as the extracellular matrix, and bacterial biofilms. Our work may also be the basis for the reconstitution of synthetic gels that mimic the basic selective properties of biological gel-based barriers.
November 2, 2017 (NOTE Thursday at 4PM): Carl Modes, MPI Molecular Cell Biology and Genetics
Complex spatial networks and programmed shape selection: Topology and geometry in biology
Host: M. Magnasco
Nature finds the means to leverage complex geometric and topologic effects in many ways that are we only now beginning to understand. For example, in the case of topology, natural transport webs are frequently dominated by dense sets of nested cycles; the architecture of these networks — the topology and edge weights — determines how efficiently the networks perform their function. We present a new characterization of these physical networks that rests on an abstraction of a physical tiling in the case of a two dimensional network to an effective tiling of an abstract surface in space that the network may be thought to sit in. This new algorithmic approach can be used for automated phenotypic characterization of any weighted network whose structure is dominated by cycles, such as, for example, mammalian vasculature in the organs, the root networks of clonal colonies like quaking aspen, and the force networks in jammed granular matter. On the geometric side of the ledger, it has recently been more and more appreciated that developing biological systems employ complicated 2D stress fields during early onset of morphogenesis from flat or quasi-flat epithelial sheets to a rich zoo of fully three dimensional objects. We discuss a speculative approach based on methods from the physics of exotic shape-shifting materials to reduce the complexity of the interacting “parts” of the stress distribution to model these developmental morphomechanics in a parameter space of drastically reduced dimensionality.
November 7, 2017: Sindy Tang, Stanford University
Order and chaos*: Collective behavior of crowded drops in microfluidic systems
Host: T. Shendruk
Droplet microfluidics, in which micro-droplets serve as individual reactors, has enabled a range of high-throughput biochemical processes. Although the physics of single drops has been studied extensively, the flow of crowded drops or concentrated emulsions—where droplet volume fraction exceeds ~80%—is relatively unexplored in microfluidics. Ability to leverage concentrated emulsions is critical for further increasing the throughput of droplet applications. Prior work on concentrated emulsions focused on their bulk rheological properties. The behavior of individual drops within the emulsion is not well understood, but is important as each droplet carries a different reaction.
This talk examines the collective behavior of drops in a concentrated emulsion by tracking the dynamics and the fate of individual drops within the emulsion. At the fast flow limit, we show that droplet breakup within the emulsion is stochastic. This contrasts the deterministic breakup in classical single-drop studies. We further demonstrate that the breakup probability is described by dimensionless numbers including the capillary number and confinement factor, and the stochasticity originates from the time-varying packing configuration of the drops. To mitigate breakup, we design novel amphiphilic nanoparticles, and show they are more effective than surfactant molecules as droplet stabilizers.
At the slow flow limit, we observe an unexpected order, where the velocity of individual drops in the emulsion exhibits spatiotemporal periodicity. Such periodicity is surprising from both fluid and solid mechanics point of view. We show the phenomenon can be explained by treating the emulsion as a soft crystal undergoing plasticity, in a nanoscale system comprising thousands of atoms as modeled by droplets. Our results represent a new type of collective order not described before, and have practical use in on-chip droplet manipulation. From the solid mechanics perspective, the phenomenon directly contrasts the stochasticity of dislocations in microscopic crystals, and suggests a new approach to control the mechanical forming of nanocrystals.
*Chaos stands for Crowded droplet breakup HydrodynAmics not Ordered but Stochastic.
This talk examines the collective behavior of drops in a concentrated emulsion by tracking the dynamics and the fate of individual drops within the emulsion. At the fast flow limit, we show that droplet breakup within the emulsion is stochastic. This contrasts the deterministic breakup in classical single-drop studies. We further demonstrate that the breakup probability is described by dimensionless numbers including the capillary number and confinement factor, and the stochasticity originates from the time-varying packing configuration of the drops. To mitigate breakup, we design novel amphiphilic nanoparticles, and show they are more effective than surfactant molecules as droplet stabilizers.
At the slow flow limit, we observe an unexpected order, where the velocity of individual drops in the emulsion exhibits spatiotemporal periodicity. Such periodicity is surprising from both fluid and solid mechanics point of view. We show the phenomenon can be explained by treating the emulsion as a soft crystal undergoing plasticity, in a nanoscale system comprising thousands of atoms as modeled by droplets. Our results represent a new type of collective order not described before, and have practical use in on-chip droplet manipulation. From the solid mechanics perspective, the phenomenon directly contrasts the stochasticity of dislocations in microscopic crystals, and suggests a new approach to control the mechanical forming of nanocrystals.
*Chaos stands for Crowded droplet breakup HydrodynAmics not Ordered but Stochastic.
November 14, 2017: Aparna Baskaran, Brandeis University
Active Matter: Applying the materials physics paradigm to biology
Host: T. Shendruk
Active matter is a term that has come to describe diverse systems from flocking animals to the cytoskeleton of a cell. In this talk I will give an overview of the theoretical paradigmthat unifies these diverse systems and discuss some results from minimal models for self propelled particles and suspension of cytoskeletal filaments.
November 16, 2017 (NOTE Thursday at 4PM): Demian Battaglia, Institute for Systems Neuroscience
Neural oscillations and information processing: From flexible routing to flexible computing
Host: C. Kirst
November 21, 2017: Jean-Pierre Eckmann, University of Geneva
Metastable states for weakly damped Hamiltonian systems
Host: T. Shendruk
Metastable motions are believed to inhibit the transport of energy through Hamiltonian, or nearly Hamiltonian, systems with many degrees of freedom. We investigate this question in a very simple model (discrete nonlinear Schroedinger equation) in which the breather solutions that are responsible for the metastable states can be computed perturbatively to an arbitrary order. Then, using a modulation hypothesis, we derive estimates for the rate at which the system drifts along a manifold of periodic orbits and verify the optimality of our estimates numerically.
November 28, 2017: Thanksgiving Break
No speaker
December 5, 2017: Amy S. Gladfelter, UNC Chapel Hill
RNA sequence controls specificity in intracellular phase separation
Host: T. Shendruk
A newly appreciated mechanism of cellular compartmentalization is the formation of liquid-like, membrane-free organelles. These dynamic compartments appear to form via a process of phase separation. Disordered proteins and in many cases RNAs condense into droplets that have specific material properties and these droplets are thought to spatially and temporally control biochemistry and protein translation. Examples of these structures include a variety of RNP granules and the nucleolus. Work in our lab has identified a key role for mRNA sequence in determining both the selectivity of droplet formation and material structures of the condensates. Recent insights into how mRNA can encode specificity and physical properties of these compartments will be presented.
December 12, 2017: Kathleen Stebe, University of Pennsylvania
Cells on curved substrates
Host: T. Shendruk
Decades of research have focused on understanding how biochemical signaling and morphogen gradients establish cell patterns during development and tissue morphogenesis. In our research, we find that geometry can drive organization and pattern formation. Mouse embryonic fibroblasts and human vascular smooth muscle cells behave differently on surfaces with various Gaussian curvatures, with pronounced changes in the organization of stress fibers and migration behaviors.
On cylindrical (zero Gaussian curvature) substrates, long, apical stress fibers within these cells align in the direction of minimum curvature, and a second subpopulation of stress fibers, located beneath the nucleus, aligns in the circumferential direction and is bent maximally. We find dramatic reorganization of the actin cytoskeleton upon activation of Rho, which is associated with increased contractility of the fibers[1]. To probe the effects of finite Gaussian curvature, we design substrates that present domains of positive Gaussian curvature connected smoothly to domains of negative Gaussian curvature. This sphere-with-skirt substrate is comprised of a positive-Gaussian curvature spherical cap surrounded by a negative-Gaussian curvature draping skirt that connects smoothly to a planar surface. The spherical cap and skirt both have principal radii similar to cell length scales. Fibroblasts cultured on this substrate exhibit significant reconfiguration of two subpopulations of stress fibers in response to curvature. Cell migration is also strongly connected to the Gaussian curvature. Cells migrating on skirts approach the cap, repolarize to establish a leading edge in the azimuthal direction, and migrate azimuthally around the feature, nearly perpendicular to the apical stress fiber alignment. This mode of migration differs from cell migration on planar surfaces, in which cells typically move in the same direction as the apical stress fiber orientation. Thus, finite Gaussian curvature not only affects the alignment of stress fiber subpopulations, but also directs migration in a manner that deviates from migration on planar surfaces.
We infer that stress fiber alignment is likely a result of a complex balance between energy penalties associated with stress fiber bending, contractility, and the dynamics of F-actin assembly. With a deeper understanding of how cells sense and respond to macroscale curvature, we may be able to use geometric cues to guide cell behaviors such as migration and contraction.
[1] Bade, N. D., Kamien, R. D., Assoian, R. K., & Stebe, K. J. (2017). Curvature and Rho activation differentially control the alignment of cells and stress fibers. Science Advances, 3(9), e1700150.
On cylindrical (zero Gaussian curvature) substrates, long, apical stress fibers within these cells align in the direction of minimum curvature, and a second subpopulation of stress fibers, located beneath the nucleus, aligns in the circumferential direction and is bent maximally. We find dramatic reorganization of the actin cytoskeleton upon activation of Rho, which is associated with increased contractility of the fibers[1]. To probe the effects of finite Gaussian curvature, we design substrates that present domains of positive Gaussian curvature connected smoothly to domains of negative Gaussian curvature. This sphere-with-skirt substrate is comprised of a positive-Gaussian curvature spherical cap surrounded by a negative-Gaussian curvature draping skirt that connects smoothly to a planar surface. The spherical cap and skirt both have principal radii similar to cell length scales. Fibroblasts cultured on this substrate exhibit significant reconfiguration of two subpopulations of stress fibers in response to curvature. Cell migration is also strongly connected to the Gaussian curvature. Cells migrating on skirts approach the cap, repolarize to establish a leading edge in the azimuthal direction, and migrate azimuthally around the feature, nearly perpendicular to the apical stress fiber alignment. This mode of migration differs from cell migration on planar surfaces, in which cells typically move in the same direction as the apical stress fiber orientation. Thus, finite Gaussian curvature not only affects the alignment of stress fiber subpopulations, but also directs migration in a manner that deviates from migration on planar surfaces.
We infer that stress fiber alignment is likely a result of a complex balance between energy penalties associated with stress fiber bending, contractility, and the dynamics of F-actin assembly. With a deeper understanding of how cells sense and respond to macroscale curvature, we may be able to use geometric cues to guide cell behaviors such as migration and contraction.
[1] Bade, N. D., Kamien, R. D., Assoian, R. K., & Stebe, K. J. (2017). Curvature and Rho activation differentially control the alignment of cells and stress fibers. Science Advances, 3(9), e1700150.
January 12, 2016: Alexei Koulakov, Cold Spring Harbor Lab
Neural relativity principle
Host: P. Sulc
January 19, 2016: Sean Eddy, Harvard University
Sequence homology searches: the future of deciphering the past
Host: P. Sulc
Computational recognition of distant sequence homology is a key to studying ancient events in molecular evolution. The better our sequence analysis methods are, the deeper in evolutionary time we can see. A major aim in the field is to improve the resolution of homology recognition methods by building increasingly realistic, complex, parameter-rich models. I will describe current and future research in protein, DNA, and RNA homology search algorithms based on probabilistic inference methods, using hidden Markov models (HMMs) and stochastic context-free grammars (SCFGs). We make these methods available in the HMMER and Infernal software from my laboratory, in collaboration with sequence family databases including Pfam and Rfam.
January 26, 2016: Leonid L. Moroz, University of Florida
Origins and Convergent Evolution of Neural Systems: From Single-neuron Genomics to NeuroSystematics
Host: C. Kirst
Neurons are different not only because they have different functions but also because they might have different genealogies. However, the enormous diversity of neurons both within the same nervous system and across species presents tremendous challenges for their unbiased classification. Here, I will discuss novel approaches and algorithms toward establishing the natural classification of neurons. Our research strategy is based upon (1) high-throughput single-cell RNA-seq and single-cell epigenomic analyses of entire neuronal circuits as they learn and remember, and (2) implementation of stochastic approaches from phylogenomics. Our results suggest that different classes of neurons and synapses (as well as complex brains) might have evolved more than once (convergent evolution) and allow us to start reconstruction the genealogy of neurons, trace ancestral cell lineages, and establish the natural classification of neurons within neural circuits across the majority of animal phyla: from ctenophores and cnidarians to bilaterians including cephalopods. The field of Neurosystematics is emerging. This might be an analog of the periodic table for neurons, with the predictive power to delineate novel neuronal phenotypes and fundamental constraints on the origins and parallel evolution of neural systems. In summary, there is more than one way to develop neuronal complexity, and animals frequently use different molecular toolkits to achieve similar functional outcomes.
January 28, 2016: Andrea Liu, University of Pennsylvania
Tuning Mechanical Response in Disordered Networks
Host: P. Sulc
The properties of amorphous solids are essentially and qualitatively different from those of simple crystals. Unless a crystal’s unit cell is very complicated, all particles or inter-particle bonds contribute nearly equally to any global quantity, so that each bond plays a similar role in determining the physical properties of the solid. For example, removing a single bond in a perfectly ordered array or network decreases the overall elastic strength of the system, but in such a way that the resistance to shear and the resistance to compression drop in tandem so that their ratio is nearly unaffected. Disordered materials are not similarly constrained. We introduce a principle unique to disordered solids wherein the contribution of any bond to one global perturbation is uncorrelated with its contribution to another. Coupled with sufficient variability in the contributions of different bonds, this “independent bond-level response” paves the way for the design of real materials with unusual and exquisitely tuned properties. For example, we can tune a disordered network’s Poisson ratio anywhere between the auxetic and incompressible limits. We can also produce a targeted response at a local scale; by perturbing the positions of pair of particles at one point we can tune in a desired response a large distance away. This response is reminiscent of allosteric regulation in proteins where a reaction at one site activates another site of the protein molecule. Experimentally, we have successfully demonstrated such mechanical networks in 2D (by laser cutting) or in 3D (3D printing).
February 4, 2016 (Thursday, 2 PM): Sonya Hanson, Memorial Sloan Kettering Cancer Center
Understanding the physical basis for biological temperature sensing
Host: E. Siggia
Engineered sensors allow us to accurately measure external stimuli, such as temperature, pressure, light, and sound. However, how biological organisms detect some of these same stimuli remains poorly understood. One of the most fundamental of these is the detection of temperature: avoiding cell damage while seeking optimal temperatures for cell physiology is key to the survival of both complex and simple organisms. While temperature affects many rates within the cell, certain components have evolved specifically to function as temperature sensors. While we know these temperature sensors exist in various forms, such as the changing of membrane fluidity, folding of RNA thermometers, or opening of ion channel pores, our understanding of how these distinct mechanisms are invoked by identical changes in temperature is incomplete. Did evolution harness the same statistical mechanical principles for all of these? For some models of temperature sensing, parallels can be drawn to problems in protein folding. This talk will walk through recent developments relevant to understanding temperature sensing in biology. First will be an overview of the exciting advances of the last decade in understanding the TRP channel family: a temperature sensing family of ion channel proteins (present only in eukaryotes), in which hot and cold sensitive channels are also sensitive to the small molecules capsaicin (of chili peppers) and menthol (of mint), respectively. Then we will examine the variety of tools initially developed to perform and analyze long timescale molecular simulations for protein folding applied to model protein systems chosen to refine the statistical mechanical understanding of small molecule binding to proteins. The talk will conclude with a look forward to how we can combine these tools to define model temperature sensing systems and develop a general framework for understanding temperature sensing mechanisms across biological organisms.
February 9, 2016 (2 PM): Tyler Shendruk, University of Oxford
From Single Swimmers to Spontaneous Spin-States
Host: E. Siggia
For the most part microbial life does not swim through simplebulk Newtonian fluids but rather moves through complex environments. Surfaces, interfaces and confinement are ubiquitous in microbial environments . Motile microbes can be either aided or deterred from reaching surfaces by external flows, and the flowing medium may exhibit a dual fluidic and elastic nature. In this seminar, we will discuss the dynamics and hydrodynamics of self-propelled microorganisms in these biologically relevant environments. In particular, swimmer trajectories within flowing films will be considered in order to determine how swimming strategy results in differing swimmer distributions. It will be shown that the rheology of flowing biological medium plays an important role in setting the ability of microbes to swim upstream in microchannels. We will then turn our attention to suspensions of many swimmers confined within an array of responsive obstacles to explore the ability of collective motion to restructure micro-environments. We model the dense suspensions of motile cells as a complex fluid: an active nematic liquid crystal. Our simulations show that a lattice of rotors immersed a bacterial suspension in self-organizes into a spin-state wherein neighboring disc-like obstacles continuously rotate in permanent alternating directions due to combined hydrodynamic and elastic effects. This antiferromagnetic spin-state is permanent and only exists for sufficiently small inter-disc spacing. The existence of such active matter-mediated forces between passive bodies suggests the ability of living suspensions to facilitate collective motion by altering their surroundings.
March 1, 2016: Ned Wingreen, Princeton University [Weiss Research Building, Room 301]
Getting together: What can enzyme clustering do for metabolism?
Host: P. Sulc
Metabolism is the set of enzymatic reactions that cells use to generate energy and biomass. Interestingly, recent studies suggest that many metabolic enzymes assemble into large clusters, often in response to environmental conditions. Theoretically, we find that large-scale enzyme clusters, with no internal spatial ordering of enzymes, offer many of the same advantages as direct substrate channeling: accelerating intermediate processing, protecting intermediates from degradation/cross-reactions, and protecting the cell from toxic intermediates. The model predicts the separation and size of coclusters that maximize metabolic efficiency. For direct validation, we study a metabolic branch point in Escherichia coli and experimentally confirm the model predictions. Our studies establish a quantitative framework to understand coclustering-mediated metabolic channeling and its application to both efficiency improvement and metabolic regulation.
March 8, 2016: Michael Laub, MIT [Carson Family Auditorium]
Specificity and evolution of protein-protein interfaces
Host: P. Sulc
Protein-protein interactions are critical to the operation and functions of all cells. The specificity of these interactions is often dictated at the level of molecular recognition, meaning proteins have an intrinsic ability to discriminate cognate from non-cognate partners. Understanding precisely how this discrimination is accomplished remains a major problem, particularly for paralogous protein families in which the individual members share high sequence and structural similarity. Our work tackles this problem primarily in the context of two-component signal transduction systems, the predominant form of signaling in bacteria, and more recently with toxin-antitoxin systems, also found throughout the bacterial kingdom. I will describe our work using analyses of amino acid coevolution to pinpoint the molecular basis of specificity in these proteins. This work has enabled the rational rewiring of protein-protein interactions and signal transduction pathways. Additionally, these studies have driven efforts to systematically map sequence spaces and probe the selective pressures and constraints that govern the evolution of protein-protein interactions.
March 22, 2016: Claude Desplan, NYU [Weiss Research Building, Room 301]
Evolution of color and motion vision
Host: P. Sulc
Like the human retina, the Drosophila retina contains randomly distributed color photoreceptor cells that are defined by the expression of different color sensitive Rhodopsins. In Drosophila, two types of individual unit eyes (ommatidia) are specified by stochastic expression of the transcription factor Spineless. This decision is controlled by a two-step process: First, each allele of spineless randomly makes a cell-intrinsic ON/OFF expression decision governed by global activation coupled with stochastic repression. When the expression decisions disagree (one allele ON and one allele OFF), interchromosal communication coordinates expression state between the two alleles. This effect does not depend on chromosomal pairing or endogenous spineless chromosomal position but instead requires specific DNA elements to mediate regulatory interactions. This mechanism couples stochastic repression with interallelic coordination and contrasts starkly with the noisy activation mechanisms seen in bacteria, and the mono-allelic, stochastic activation mechanisms observed in the mouse olfactory and human color vision systems.
Many vertebrate and invertebrate eyes also have retinal mosaics that contain different stochastically specified types of photoreceptors. At least one group, the butterflies, make a three-way stochastic choice between three ommatidial types. However, it remains unclear how much of the regulatory network that specifies photoreceptor subtypes is retained or has evolved in other insects, and whether they use stochastic Spineless expression to diversify their sometimes more complex retinal mosaics. I will present evidence that a conserved regulatory code defines and expands photoreceptor subtypes between flies (Drosophila) and butterflies (Papilio xuthus and Vanessa cardui). We used CRISPR/Cas9 to knock out Spineless in butterflies and provide functional evidence that there is deep evolutionary conservation of stochastic patterning mechanisms. Furthermore, butterflies have two R7 photoreceptors that allow for the specification of three types of ommatidia instead of two. This in turn allowed for the evolution and deployment of additional opsins, tetrachromacy, and improved color vision, important features of butterfly life history and ecology. Our extensive knowledge of patterning in the Drosophila visual system applies to other groups, and adaptation for specific visual requirements can occur through modification of this network.
Patterning of the neurons that process visual information in the optic lobes is, in contrast to the retina, highly deterministic. The medulla, where motion and color information are processed, contains 40,000 neurons of more than 70 cell types. These neurons are born from neural stem cells (Neuroblasts) that sequentially express five transcription factors. The neurons emerging from neuroblasts at each stage maintain expression of the corresponding gene and become different cell types. We will describe the mechanisms controlling the transition from one neuroblast stage to the next. The neuroepithelium that generates the medulla neuroblasts is also highly patterned, with eight regions defined by the expression of spatially restricted transcription factors. Each region contributes to generating two types of neurons: ‘Uni-columnar neurons’ that have a 1:1 stoichiometry with the photoreceptors that innervate the medulla and are generated throughout the neuroepithelium. The less numerous ‘non-columnar’ neurons, which contact multiple photoreceptors, are generated from specific sub-regions of the neuroepithelium and migrate to take on their retinotopic position in the medulla. This combination of temporal and spatial patterning allows for the generation of the 70 medulla cell types.
Many vertebrate and invertebrate eyes also have retinal mosaics that contain different stochastically specified types of photoreceptors. At least one group, the butterflies, make a three-way stochastic choice between three ommatidial types. However, it remains unclear how much of the regulatory network that specifies photoreceptor subtypes is retained or has evolved in other insects, and whether they use stochastic Spineless expression to diversify their sometimes more complex retinal mosaics. I will present evidence that a conserved regulatory code defines and expands photoreceptor subtypes between flies (Drosophila) and butterflies (Papilio xuthus and Vanessa cardui). We used CRISPR/Cas9 to knock out Spineless in butterflies and provide functional evidence that there is deep evolutionary conservation of stochastic patterning mechanisms. Furthermore, butterflies have two R7 photoreceptors that allow for the specification of three types of ommatidia instead of two. This in turn allowed for the evolution and deployment of additional opsins, tetrachromacy, and improved color vision, important features of butterfly life history and ecology. Our extensive knowledge of patterning in the Drosophila visual system applies to other groups, and adaptation for specific visual requirements can occur through modification of this network.
Patterning of the neurons that process visual information in the optic lobes is, in contrast to the retina, highly deterministic. The medulla, where motion and color information are processed, contains 40,000 neurons of more than 70 cell types. These neurons are born from neural stem cells (Neuroblasts) that sequentially express five transcription factors. The neurons emerging from neuroblasts at each stage maintain expression of the corresponding gene and become different cell types. We will describe the mechanisms controlling the transition from one neuroblast stage to the next. The neuroepithelium that generates the medulla neuroblasts is also highly patterned, with eight regions defined by the expression of spatially restricted transcription factors. Each region contributes to generating two types of neurons: ‘Uni-columnar neurons’ that have a 1:1 stoichiometry with the photoreceptors that innervate the medulla and are generated throughout the neuroepithelium. The less numerous ‘non-columnar’ neurons, which contact multiple photoreceptors, are generated from specific sub-regions of the neuroepithelium and migrate to take on their retinotopic position in the medulla. This combination of temporal and spatial patterning allows for the generation of the 70 medulla cell types.
March 24, 2016: Jane Wang, Cornell [Carson Family Auditorium]
Insect Flight: From Newton’s Law to Neurons
Host: P. Sulc
Why do animals move the way they do? Bacteria, insects, birds, and fish share with us the necessity to move so as to live. Although each organism follows its own evolutionary course, it also obeys a set of common laws. At the very least, the movement of animals, like that of planets, is governed by Newton’s law: All things fall. On Earth, most things fall in air or water, and their motions are thus subject to the laws of hydrodynamics. Through trial and error, animals have found ways to interact with fluid so they can float, drift, swim, sail, glide, soar, and fly. This elementary struggle to escape the fate of falling shapes the development of motors, sensors, and mind. Perhaps we can deduce parts of their neural computations by understanding what animals must do so as not to fall.
In this talk I will discuss recent developments along this line of inquiry in the case of insect flight. Asking how often a fly must sense its orientation in order to balance in air has shed new light on the role of motor neurons and steering muscles responsible for flight stability.
In this talk I will discuss recent developments along this line of inquiry in the case of insect flight. Asking how often a fly must sense its orientation in order to balance in air has shed new light on the role of motor neurons and steering muscles responsible for flight stability.
April 5, 2016: Michael Hagan, Brandeis University [Carson Family Auditorium]
Cargo encapsulation by self-assembling icosahedral containers
Host: P. Sulc
The self-assembly of a protein shell around a cargo is a common mechanism of encapsulation in biology, and is inspiring development of drug delivery vehicles that form by self-assembly. However, the physics underlying such multicomponent assembly processes is incompletely understood. In this talk I will describe how minimal computational models can elucidate two biological examples in which icosahedral protein shells assemble around cargos. In each case we find that the material properties of the cargo play a key role in directing its encapsulation.
The first example concerns viruses with single-stranded RNA (ssRNA) genomes. For many ssRNA viruses, formation of an infectious virus requires the spontaneous assembly of an icosahedral protein shell (called a capsid) around the genome. I will describe simulations that investigate how this co-assembly process depends on the physical properties of RNA: its length, electrostatic charge, and 3D structure. When applied to specific virus capsids, the calculated optimal RNA lengths closely correspond to the natural viral genome lengths. This suggests that evolution of viral RNA is driven not only by the fitness of the proteins that it encodes for, but also by how its material properties favor encapsulation. We then show that assembly can proceed through two qualitatively different classes of pathways, which can be tuned by solution conditions or changing the capsid protein properties.
The second example concerns carboxysomes, which are large, roughly icosahedral protein shells that facilitate carbon fixation in cyanobacteria. Carboxysomes assemble around a cargo which is topologically different from ssRNA, a noncovalently linked, amorphous complex of the enzyme RuBisCO. Motivated by this problem, we study assembly of icosahedral shells around a fluid cargo. We find different assembly pathways and different critical control parameters as compared to assembly around RNA, and that the predominant assembly pathway depends strongly on the cargo fluidity. We discuss relationships between simulated assembly pathways and recent experiments observing assembly of individual carboxysomes in bacteria.
The first example concerns viruses with single-stranded RNA (ssRNA) genomes. For many ssRNA viruses, formation of an infectious virus requires the spontaneous assembly of an icosahedral protein shell (called a capsid) around the genome. I will describe simulations that investigate how this co-assembly process depends on the physical properties of RNA: its length, electrostatic charge, and 3D structure. When applied to specific virus capsids, the calculated optimal RNA lengths closely correspond to the natural viral genome lengths. This suggests that evolution of viral RNA is driven not only by the fitness of the proteins that it encodes for, but also by how its material properties favor encapsulation. We then show that assembly can proceed through two qualitatively different classes of pathways, which can be tuned by solution conditions or changing the capsid protein properties.
The second example concerns carboxysomes, which are large, roughly icosahedral protein shells that facilitate carbon fixation in cyanobacteria. Carboxysomes assemble around a cargo which is topologically different from ssRNA, a noncovalently linked, amorphous complex of the enzyme RuBisCO. Motivated by this problem, we study assembly of icosahedral shells around a fluid cargo. We find different assembly pathways and different critical control parameters as compared to assembly around RNA, and that the predominant assembly pathway depends strongly on the cargo fluidity. We discuss relationships between simulated assembly pathways and recent experiments observing assembly of individual carboxysomes in bacteria.
April 12, 2016: Jonathon Howard, Yale University [Weiss Research Building, Room 301]
Beat Generation: Ciliary and Flagellar Motion Driven by Cooperative Molecular Motors
Host: P. Sulc
The beating patterns of sperm flagella and the breast-stroke like swimming of ciliates are driven by the molecular motor dynein. This motor generates sliding forces between adjacent microtubule doublets within the axoneme, the motile cytoskeletal structure. To create regular, oscillatory beating patterns, the activities of the dyneins must be coordinated both spatially and temporally. It is thought that coordination is mediated by stresses or strains that build up within the moving axoneme, but it is not known which components of stress or strain are involved, nor how they feed back on the dyneins. To answer this question, we measured the beating patterns of isolated, reactivate axonemes of the unicellular alga Chlamydomonas. We compared the measurements in wildtype and mutant cells with models derived from different feedback mechanisms. We found that regulation by changes in axonemal curvature was the only mechanism that accords with the measurements.
April 21, 2016: Luca Cardelli, Microsoft Research [Carson Family Auditorium]
Noise Reduction in Complex Biological Switches
Host: P. Sulc
Cells operate in noisy molecular environments via complex regulatory networks. It is possible to understand how molecular counts are related to noise in specific networks, but it is not generally clear how noise relates to network complexity, because different levels of complexity also imply different overall number of molecules. For a fixed function, does increased network complexity reduce noise, beyond the mere increase of overall molecular counts? If so, complexity could provide an advantage counteracting the costs involved in maintaining larger networks.
For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches like the G2/M cell cycle transition switch. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces.
We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.
For that purpose, we investigate how noise affects multistable systems, where a small amount of noise could lead to very different outcomes; thus we turn to biochemical switches like the G2/M cell cycle transition switch. Our method for comparing networks of different structure and complexity is to place them in conditions where they produce exactly the same deterministic function. We are then in a good position to compare their noise characteristics relatively to their identical deterministic traces.
We show that more complex networks are better at coping with both intrinsic and extrinsic noise. Intrinsic noise tends to decrease with complexity, and extrinsic noise tends to have less impact. Our findings suggest a new role for increased complexity in biological networks, at parity of function.
April 26, 2016: Arup Chakraborty, MIT [Carson Family Auditorium]
How to hit HIV where it hurts
Host: P. Sulc
HIV continues to wreak havoc around the world, especially in poor countries. A vaccine is urgently needed to overcome this major global health challenge. I will describe key challenges that must be confronted to achieve this goal. I will then focus on some work that aims to address a part of these challenges by bringing together theory and computation (rooted in statistical physics), consideration of structures of multi-protein assemblies, basic immunology, and human clinical data. The results of these studies suggest the design of immunogens and immunization strategies for vaccines that might elicit immune responses which might be able to hit HIV where it hurts upon natural infection.
April 28, 2016: Rémi Monasson, Simona Cocco, ENS, Paris [Weiss Research Building, Room 301]
Benchmarking inverse statistical approaches for protein structure and
design with exactly solvable models
design with exactly solvable models
Host: P. Sulc
Inverse statistical approaches, modeling pairwise correlations between amino acids in the sequences of homologous proteins across many different organisms, can successfully extract protein structure (contact) information. Here, we benchmark those statistical approaches on exactly solvable models of proteins, folding on a 3D lattice, to assess the reasons underlying their success and their limitations. We show that the inferred parameters (effective pairwise interactions) of the statistical models have clear and quantitative interpretations in terms of positive (favoring the native fold) and negative (disfavoring competing folds) protein sequence design. New sequences randomly drawn from the statistical models are likely to fold into the native structures when effective pairwise interactions are accurately inferred, a performance which cannot be achieved with independent-site models.
May 3, 2016: Hiro Matsunami, Duke University [Weiss Research Building, Room 301]
Mammalian odorant receptors: deorphanization, trafficking and gene choice
Host: M. Magnasco
Impressive progress in membrane biology has revealed important relationships in G-protein coupled receptor (GPCR) structure and function. Despite this, understanding of the largest family of GPCRs, the mammalian odorant receptors (ORs), lags behind. To date, there is no crystal structure of any ORs and many remain orphans without known ligands. These challenges must be overcome in order to understand the molecular mechanisms of olfaction.
We have developed a new strategy that enables comprehensive screening of ORs in freely behaving animals by odor stimulation. We combine phosphorylated ribosomal protein S6 immunoprecipitation with next generation sequencing to profile OR expression in active olfactory sensory neurons. This method is capable of not only identifying a repertoire of odorant-OR pairs, but also reveals the most robust and sensitive ORs. This deorphanization is key to understanding structure-function relationships of odorant-OR interactions.
Poor cell surface expression of ORs in heterologous cells represents a major challenge in functional studies of ORs. We previously identified the RTP family of accessory proteins that facilitate OR cell surface expression, and used them for functional analysis in heterologous cells. A transcriptomic and in situ hybridization analysis of olfactory mucosa of RTP1 and RTP2 knockout mice has revealed that the majority of ORs are downregulated, whereas a small subset of ORs are upregulated. This subset of ORs demonstrate expression at the cell surface in heterologous systems, suggesting an unexpected connection between OR protein trafficking and gene choice.
We have developed a new strategy that enables comprehensive screening of ORs in freely behaving animals by odor stimulation. We combine phosphorylated ribosomal protein S6 immunoprecipitation with next generation sequencing to profile OR expression in active olfactory sensory neurons. This method is capable of not only identifying a repertoire of odorant-OR pairs, but also reveals the most robust and sensitive ORs. This deorphanization is key to understanding structure-function relationships of odorant-OR interactions.
Poor cell surface expression of ORs in heterologous cells represents a major challenge in functional studies of ORs. We previously identified the RTP family of accessory proteins that facilitate OR cell surface expression, and used them for functional analysis in heterologous cells. A transcriptomic and in situ hybridization analysis of olfactory mucosa of RTP1 and RTP2 knockout mice has revealed that the majority of ORs are downregulated, whereas a small subset of ORs are upregulated. This subset of ORs demonstrate expression at the cell surface in heterologous systems, suggesting an unexpected connection between OR protein trafficking and gene choice.
May 10, 2016: Alexandre Pouget, Université de Genève [Weiss Research Building, Room 301]
Learning and demixing in the olfactory system
Host: C. Kirst
May 17, 2016: Nicolas Brunel, University of Chicago [Carson Family Auditorium]
Inferring learning rules in cortical circuits
Host: C. Kirst
Understanding the mechanisms of learning and memory is one of the major challenges in neuroscience. The dominant theory holds that information about sensory inputs is stored in cortical circuits thanks to synaptic plasticity. In spite of decades of research, the exact rules governing how synapses change as a function of the activity of pre and post-synaptic neurons remain the subject of debate. In this talk, I will present two novel approaches for investigating the mechanisms of learning and memory. The first consists in inferring a learning rule from in vivo data, using experiments comparing the statistics of responses of neurons to large sets of novel and familiar stimuli. The second consists in exploring the consequences of an information optimization principle on the statistics of synaptic connectivity. I will show how methods from statistical physics can be used to characterize the statistics of connectivity in networks that optimize information storage, and compare the theoretical results with available data.
May 24, 2016: Christopher Jarzynski, University of Maryland [Weiss Research Building, Room 301]
Irreversibility, information and the second law of thermodynamics at the nanoscale
Host: P. Sulc
What do the laws of thermodynamics look like, when applied to microscopic systems such as optically trapped colloidal particles, single molecules manipulated with laser tweezers, and biomolecular machines? In recent years it has become apparent that the fluctuations of small systems far from thermal equilibrium satisfy strong and unexpected laws, which allow us to rewrite familiar inequalities of macroscopic thermodynamics as equalities. These results in turn have spurred a renewed interest in the feedback control of small systems and the closely related Maxwell’s demon paradox. I will describe some of this progress, and will argue that it has refined our understanding of irreversibility, the second law, and the thermodynamic arrow of time.
May 26, 2016: Jean-Pierre Eckmann, University of Geneva [Carson Family Auditorium]
The geometry of the genotype-to-phenotype map of proteins: dimension, correlation and spectrum
Host: P. Sulc
I will report on work with Tsvi Tlusty on how one can envisage a concrete map between the genetic information contained in the DNA sequence and mechanical function of the protein.
The simplicity of our model allows for an extensive survey of the “protein universe”, spanning a huge number of generations and samples, far beyond what can be done in real living matter.
This helps in understanding how mechanical constraints on the function of the protein force the system into a very small subset of possible states. Evolution can be described in terms of a low-dimensional random walk on this subset of an almost infinite-dimensional space. This is also the origin of tight correspondence between the spectrum of functional DNA sequences and the mechanical modes of the protein.
The simplicity of our model allows for an extensive survey of the “protein universe”, spanning a huge number of generations and samples, far beyond what can be done in real living matter.
This helps in understanding how mechanical constraints on the function of the protein force the system into a very small subset of possible states. Evolution can be described in terms of a low-dimensional random walk on this subset of an almost infinite-dimensional space. This is also the origin of tight correspondence between the spectrum of functional DNA sequences and the mechanical modes of the protein.
September 20, 2016: Alex Mogilner, NYU
Spontaneous and induced cell polarization and collective migration
Host: P. Sulc
Fish keratocyte cells served as the model system to understand biophysics of cell motility for decades. Recently, we combined experiment and modeling to understand the mechanism of polarization of these cells. We found that two essential feedbacks – positive one between myosin density and actin flow, and negative one between stick-slip adhesions and actin flow – underlie the motility initiation. Interestingly, keratocytes polarize in electric fields much faster but not stably, through different mechanism. I will also describe preliminary results on collective keratocyte migration in electric fields.
September 27, 2016: David Pine, NYU
DNA-directed self-assembly of colloidal crystals: diamond and pyrochlore
Host: P. Sulc
Coating colloidal particles with DNA is emerging as a new way to direct self assembly. In principle, it allows one to program the assembly of different materials in almost any way imaginable. Here we describe recent progress, which includes the introduction of valence and novel superlattices to create new colloidal structures for photonic applications.
October 4, 2016: Philip Kim, University of Toronto
Integrating computational and experimental methods in proteomics and drug discovery”
Host: P. Sulc
I will present our advances in combining computational and experimental techniques to develop novel inhibitors. We have developed an integrated pipeline that first computationally designs large libraries of potential inhibitors and can then screen these for either cellular phenotype or high affinity binding. I will showcase this pipeline on two example applications, first for developing inhibitors to protein-protein interactions and second for developing novel high-affinity biologics.
October 11, 2016: Mehran Kardar, MIT
Force from non-equilibrium fluctuations in QED and Active Matter
Host: P. Sulc
Equilibrium fluctuation-induced forces are abundant in nature, ranging from quantum electrodynamic (QED) Casimir and van der Waals forces, to their thermal analogs in fluctuating soft matter. Manifestations of QED fluctuations out of thermal equilibrium are also well-known, as in the Stefan-Boltzmann laws of radiation pressure and heat transfer. These laws, however, acquire non-trivial twists in the near-field regime of sub-micron separations, and in the proximity of moving surfaces. I will discuss dissipation in moving steady states, and the non-Gaussian fluctuations of a particle in a quantum bath.
Non-equilibrium fluctuation forces for particulate matter also hold surprises which I present in the contexts of diffusive transport, and active matter: For the simple case of a current of diffusive particles between parallel slabs, we find a force that falls off with slab separation d as kT/d (at temperature T, and in all spatial dimensions), but that can be attractive or repulsive. There is also a universal transient force when a system of particles undergoes temperature quench or sudden agitation. For a wide wide class of active systems, we find that the pressure exerted on a container depends on details of interactions with the confining walls, as well as wall curvature and asymmetry.
Non-equilibrium fluctuation forces for particulate matter also hold surprises which I present in the contexts of diffusive transport, and active matter: For the simple case of a current of diffusive particles between parallel slabs, we find a force that falls off with slab separation d as kT/d (at temperature T, and in all spatial dimensions), but that can be attractive or repulsive. There is also a universal transient force when a system of particles undergoes temperature quench or sudden agitation. For a wide wide class of active systems, we find that the pressure exerted on a container depends on details of interactions with the confining walls, as well as wall curvature and asymmetry.
October 18, 2016: Yuhai Tu, IBM Research
Physics of information processing in living systems: on sensory adaptation and biological oscillations
Host: P. Sulc
Living organisms need to obtain and process information that are crucial for their survival. Information processing in living systems, ranging from signal transduction in a single cell to image processing in the human brain, are performed by biological circuits (networks of interacting bio-molecules or neurons), which are inherently noisy. However, certain accuracy is required to carry out proper biological functions. How do biological networks process information with noisy components? What is the free energy cost of accurate biological computing? Are there fundamental physics principles underlying the performance of these biological circuits? In this talk, we will describe our recent work in trying to address these general questions in the context of two basic cellular computing tasks: sensory adaptation for memory encoding; biochemical oscillation for accurate timekeeping.
October 25, 2016: Alexander Grosberg, NYU
Activity induced phase separation in particles and (bio)polymers
Host: P. Sulc
Particles may phase separate because they are of different sizes, of different shapes, or interact differently. But there is also the possibility that they separate based on the different level of activity. I will present a simple model which illustrates this idea. I will also discuss how situation changes if particles are connected to form a polymer chain, and speculate about possible implications of these results.
November 1, 2016: Jeff Hammerbacher, Icahn Institute at Mount Sinai
Tumor neoepitope selection for biomarker discovery and therapeutic vaccination
Host: P. Sulc
We’ll review some open source software our lab has developed to facilitate a Phase I clinical trial of a personalized therapeutic vaccine targeting tumor neoepitopes. We’ll also present some results from our analysis of clinical trials of checkpoint blockade in 3 different cancer types and some open source software we’ve created to facilitate similar analyses.
November 8, 2016: Eric Vanden-Eijnden, NYU
Non-equilibrium transitions between metastable patterns in populations of motile bacteria
Host: P. Sulc
Active materials can self-organize in many more ways than their equilibrium counterparts. For example, self-propelled particles whose velocity decreases with their density can display motility-induced phase separation (MIPS), a phenomenon building on a positive feedback loop in which patterns emerge in locations where the particles slow down. Here, we investigate the effects of intrinsic fluctuations in the system’s dynamics on MIPS, using a field theoretic description building on results by Cates and collaborators. We show that these fluctuations can lead to transitions between metastable patterns. The pathway and rate of these transitions is analyzed within the realm of large deviation theory, and they are shown to proceed in a very different way than one would predict from arguments based on detailed-balance and microscopic reversibility. Specifically, we show that these transitions involve fluctuations in diffiusivity of the bacteria followed by fluctuations in their population, in a specific sequence. The method of analysis proposed here, including its numerical component, can be used to study noise-induced non-equilibrium transitions in a variety of other non-equilibrium set-ups, and leads to predictions that are verifiable experimentally.
November 15, 2016: Elodie Ghedin (NYU)
Dynamics of influenza virus transmission
Host: P. Sulc
Abstract: The characterization of virus populations by deep sequencing is transforming our understanding of viral evolutionary dynamics. This enables us to address questions about the extent of within-host virus diversity and what proportion of this diversity is transmitted between infected hosts. Using the same tools we can also query the host environment in which the virus evolves—such as host microbial ecology and local response to infection—to determine its effect on virus evolution. I will discuss how we quantify virus diversity and characterize virus variants that achieve sustainable transmission, and illustrate how immune status, the respiratory microbiome, and mixed infections can shape influenza virus transmission.
November 22, 2016: Armita Nourmohammad (Princeton University)
Effective theory for immune-pathogen coevolution
Host: P. Sulc
We normally think of evolution occurring in a population of organisms, in response to their external environment. Rapid evolution of cellular populations also occurs within our bodies, as the adaptive immune system works to eliminate infection. Some pathogens, such as HIV, are able to persist in a host for extended periods of time, during which they evolve to evade the immune response. In this talk I will introduce an analytical framework for the rapid coevolution of B-cell and viral populations. I will quantify the amount of out-of-equilibrium adaptation in each of the two populations by analysis of their co-evolutionary history. I will discuss the consequences of competition between lineages of antibodies, and characterize the fate of a given lineage dependent on the state of the antibody and viral populations. In particular, I will discuss the conditions for emergence of highly potent broadly neutralizing antibodies, which are now recognized as critical for designing an effective vaccine against HIV.
November 29, 2016: Alessandra Carbone, University of Paris VI
Conservation, co-evolution and dynamics: from sequences to functions
Host: P. Sulc
Biology entered a new era, with computational biology producing biological data that are impossible nowadays to obtain with wet experiments. Tackling biological questions with advanced engineering, new computer algorithms and novel computational approaches is a challenge that will lead to revolutionize biology and medicine through deeper, ubiquitous use of DNA information.
A fundamental question is the extraction of evolutionary information from DNA sequences. Here, we consider protein sequences and structures, and describe how important biological information on protein-protein binding sites and on mechanical and allosteric properties of proteins can be extracted. Among different examples, we shall present a computational approach to protein-protein interactions that we developed within a project on protein network reconstruction on neuromuscular diseases. The project demands a high computational power to test billions of interactions, it ran on the machines of the World Community Grid for more than 3 years, and provided a huge amount of information on the interaction of human proteins. High Performance Computing helped to obtain an unprecedented amount of information on protein-protein interactions between real partners but also, and most importantly,
between non-partners.
A fundamental question is the extraction of evolutionary information from DNA sequences. Here, we consider protein sequences and structures, and describe how important biological information on protein-protein binding sites and on mechanical and allosteric properties of proteins can be extracted. Among different examples, we shall present a computational approach to protein-protein interactions that we developed within a project on protein network reconstruction on neuromuscular diseases. The project demands a high computational power to test billions of interactions, it ran on the machines of the World Community Grid for more than 3 years, and provided a huge amount of information on the interaction of human proteins. High Performance Computing helped to obtain an unprecedented amount of information on protein-protein interactions between real partners but also, and most importantly,
between non-partners.
December 6, 2016: Eugene Shakhnovich, Harvard University
Understanding evolution on multiple scales: from protein physics to population genetics and back.
Host: P. Sulc
Biological phenomena unfold in a broad range of scales ranging from molecules to cells to populations and ecosystems. Variation of molecular properties of biomolecules profoundly impact the ability of cells to survive and propagate (fitness). Finally, the fate of a mutation is decided by Darwinian selection on the level of the population, where three outcomes are possible: fixation in the population, elimination by purifying selection or separation in the population in a subdominant clone (polymorphism). In this lecture I will outline my lab’s and others efforts in an emerging new field which merges molecular mechanism with evolution.
I will discuss new models of evolutionary dynamics on biophysical fitness landscapes. Traditional population genetics models are agnostic to the physical-chemical nature of mutational effects. Rather they operate with an a’priori assumed distributions of fitness effects (DFE) of mutations from which evolutionary dynamics are derived. In departure with this tradition the novel multiscale models integrate the molecular effects of mutations on physical properties of proteins into physically intuitive yet detailed genotype-phenotype relationship (GPR) assumptions. I will present a range of models from simple analytical diffusion-based model on biophysical fitness landscapes to more sophisticated computational models of populations of model cells where genetic changes are mapped into molecular effects using biophysical modeling of proteins and ensuing fitness changes determine the fate of mutations in realistic population dynamics. Examples of insights derived from biophysics-based multiscale models include the fundamental limit on mutation rates in living organisms, physics of thermal adaptation, co-evolution of protein interactions and abundances in cytoplasm and related results, some of which I will briefly present and discuss.
Next, I will present major results from novel “bottom-up” experimental approach to study evolutionary dynamics on biophysical fitness landscapes. The approach spans all scales of biological organization involving concurrent use of genome editing, biophysical characterization of molecular effects of mutations, high throughput proteomic analysis at the systems level and phenotypic analysis. It validates and further develops the concept of biophysical fitness landscapes by showing that certain combinations of molecular traits can serve as a universal predictor of fitness effects of mutations. Thus linking fitness effects to intermediate phenotype – molecular and cellular effects of mutations – provides a comprehensive low-dimensional mapping of genotype to phenotype – a biophysical fitness landscape – on which evolutionary dynamics unfolds.
I will discuss new models of evolutionary dynamics on biophysical fitness landscapes. Traditional population genetics models are agnostic to the physical-chemical nature of mutational effects. Rather they operate with an a’priori assumed distributions of fitness effects (DFE) of mutations from which evolutionary dynamics are derived. In departure with this tradition the novel multiscale models integrate the molecular effects of mutations on physical properties of proteins into physically intuitive yet detailed genotype-phenotype relationship (GPR) assumptions. I will present a range of models from simple analytical diffusion-based model on biophysical fitness landscapes to more sophisticated computational models of populations of model cells where genetic changes are mapped into molecular effects using biophysical modeling of proteins and ensuing fitness changes determine the fate of mutations in realistic population dynamics. Examples of insights derived from biophysics-based multiscale models include the fundamental limit on mutation rates in living organisms, physics of thermal adaptation, co-evolution of protein interactions and abundances in cytoplasm and related results, some of which I will briefly present and discuss.
Next, I will present major results from novel “bottom-up” experimental approach to study evolutionary dynamics on biophysical fitness landscapes. The approach spans all scales of biological organization involving concurrent use of genome editing, biophysical characterization of molecular effects of mutations, high throughput proteomic analysis at the systems level and phenotypic analysis. It validates and further develops the concept of biophysical fitness landscapes by showing that certain combinations of molecular traits can serve as a universal predictor of fitness effects of mutations. Thus linking fitness effects to intermediate phenotype – molecular and cellular effects of mutations – provides a comprehensive low-dimensional mapping of genotype to phenotype – a biophysical fitness landscape – on which evolutionary dynamics unfolds.
December 20, 2016 Ned Seeman, NYU
DNA: Not Merely the Secret of Life
Host: P. Sulc
The essence of Structural DNA Nanotechnology is the combination of branched DNA molecules combined with interactions that can be prescribed by Watson-Crick base pairing. The key goals of the area include the production of objects, lattices and nanomechanical devices made from DNA, as well as controlling the positions of other materials. By the middle 1990s, geometrical control was achieved, leading well-defined objects, often objects acting as tiles for 2D lattices.
Nanorobotics is a key area of application. We have made robust 2-state and 3-state sequence-dependent devices and bipedal walkers. We have constructed a molecular assembly line using a DNA origami layer and three 2-state devices, so that there are eight different states represented by their arrangements. All eight products can be built from this system.
Organization of other materials by DNA is another key goal. We have placed differently-sized gold nanoparticles in a checkerboard array in 2D, and in specific positions in 3D. We have also placed carbon nanotubes on DNA origami in specific positions.
There is an empirical rule stating that the best arrays in multidimensional DNA systems result when helix axes span each dimension. We have self-assembled a 2D crystalline origami array by applying this rule. We used the same rule to self-assemble a 3D crystalline array. We initially reported its crystal structure to 4 Å resolution, but rational design of intermolecular contacts has enabled us to improve the crystal resolution to better than 3 Å. We can use crystals with two molecules in the crystallographic repeat to control the color of the crystals. We can change the color of crystals by doing strand displacement of duplex DNA; we can also color the crystals using triplex formation. When tailed in DNA, we can add semiconductors to the crystals, and follow their transitions by crystal color. The use of the crystals to host guests promises an approach to the organization of macromolecules in 3D. Diffraction of the crystals offers a means to ascertain the successful construction of their targets and the characterization of their guests.
This Research was supported by MURI W911NF-11-1-0024 from ARO, N000141110729 from ONR, grants EFRI-1332411 and CCF-1526650 from the NSF, DE-SC0007991 from DOE for DNA synthesis and partial salary support, and grant GBMF3849 from the Gordon and Betty Moore Foundation.
Nanorobotics is a key area of application. We have made robust 2-state and 3-state sequence-dependent devices and bipedal walkers. We have constructed a molecular assembly line using a DNA origami layer and three 2-state devices, so that there are eight different states represented by their arrangements. All eight products can be built from this system.
Organization of other materials by DNA is another key goal. We have placed differently-sized gold nanoparticles in a checkerboard array in 2D, and in specific positions in 3D. We have also placed carbon nanotubes on DNA origami in specific positions.
There is an empirical rule stating that the best arrays in multidimensional DNA systems result when helix axes span each dimension. We have self-assembled a 2D crystalline origami array by applying this rule. We used the same rule to self-assemble a 3D crystalline array. We initially reported its crystal structure to 4 Å resolution, but rational design of intermolecular contacts has enabled us to improve the crystal resolution to better than 3 Å. We can use crystals with two molecules in the crystallographic repeat to control the color of the crystals. We can change the color of crystals by doing strand displacement of duplex DNA; we can also color the crystals using triplex formation. When tailed in DNA, we can add semiconductors to the crystals, and follow their transitions by crystal color. The use of the crystals to host guests promises an approach to the organization of macromolecules in 3D. Diffraction of the crystals offers a means to ascertain the successful construction of their targets and the characterization of their guests.
This Research was supported by MURI W911NF-11-1-0024 from ARO, N000141110729 from ONR, grants EFRI-1332411 and CCF-1526650 from the NSF, DE-SC0007991 from DOE for DNA synthesis and partial salary support, and grant GBMF3849 from the Gordon and Betty Moore Foundation.
January 13, 2015: Jan Skotheim, Stanford University
Cell size control
Host: M. Vucelja
Cell size is an important physiological trait that is regulated by coupling growth with cell division. Although many of the key regulatory proteins effecting size control have been identified, the underlying molecular mechanisms are unclear. I will discuss our recent progress in addressing this fundamental question in proliferating budding yeast and in developing frog embryos. The common feature of yeast and frog mechanisms is that the synthesis of activating and inhibiting molecules scales differently with cell size. Through this differential size scaling, the ratio of activator and inhibitor becomes a monotonic function of cell size so that the cellular transition becomes size-dependent. Importantly, this mechanism can function in the context of any regulatory network and any cell geometry. Thus, the differential size scaling of macromolecular synthesis provides an elegant mechanism to couple cellular transitions with cell size.
January 20, 2015: James Sethna, Cornell University
Sloppy models, Differential geometry, and How Science Works
Host: M. Vucelja
Models of systems biology, climate change, ecosystems, and macroeconomics have parameters that are hard or impossible to measure directly. If we fit these unknown parameters, fiddling with them until they agree with past experiments, how much can we trust their predictions? We have found that predictions can be made despite huge uncertainties in the parameters — many parameter combinations are mostly unimportant to the collective behavior. We will use ideas and methods from differential geometry to explain what sloppiness is and why it happens so often. Finally, we shall show that field-theory models in physics are also sloppy – that sloppiness makes science possible.
January 29, 2015:(NOTE: Thursday at 4PM!) Yariv Kafri, Technion
Pressure in Non-equilibrium (Active) Systems
Host: M. Vucelja
Pressure is the mechanical force per unit area that a confined system exerts on its container. In thermal equilibrium, the pressure depends only on bulk properties (density, temperature, etc.) through an equation of state. The talk will show that in active systems containing self-propelled particles, the pressure instead can depend on the precise interactions between the system’s contents and its confining walls. This implies that generic active systems have no equation of state. Other anomalous attributes of pressure will also be discussed.
Finally, it will be shown that in certain fine tuned cases an equation of state can be recovered. The physics behind the equation of state in a specific example will be discussed.
Finally, it will be shown that in certain fine tuned cases an equation of state can be recovered. The physics behind the equation of state in a specific example will be discussed.
February 3, 2015 (NOTE: at 2pm!): Dapeng “Max” Bi, Syracuse University
Dynamical arrest of cell motion in tissues: a constant-density rigidity transition
Host: C. Kirst
Cell migration is important in many biological processes, including embryonic development, cancer metastasis, and wound healing. In these tissues, a cell’s motion is often strongly constrained by its neighbors, leading to glassy dynamics.
While self-propelled particle models exhibit a density-driven glass transition, this does not explain liquid-to-solid transitions in confluent tissues, where there are no gaps between cells and therefore the density is constant. Here we demonstrate the existence of a new type of rigidity transition that occurs in the well-studied vertex model for confluent tissue monolayers at constant density. We find the onset of rigidity is governed by a model parameter that encodes single-cell properties such as cell-cell adhesion and cortical tension, providing an explanation for a liquid-to-solid transitions in confluent tissues and making testable predictions about how these transitions differ from those in particulate matter.
While self-propelled particle models exhibit a density-driven glass transition, this does not explain liquid-to-solid transitions in confluent tissues, where there are no gaps between cells and therefore the density is constant. Here we demonstrate the existence of a new type of rigidity transition that occurs in the well-studied vertex model for confluent tissue monolayers at constant density. We find the onset of rigidity is governed by a model parameter that encodes single-cell properties such as cell-cell adhesion and cortical tension, providing an explanation for a liquid-to-solid transitions in confluent tissues and making testable predictions about how these transitions differ from those in particulate matter.
February 10, 2015 (NOTE: at 2pm!): Maxim Imakaev, MIT
Organization of human chromosomes across the cell cycle
Host: C. Kirst
The study of chromosomes has a rich history; however, until recently, the internal structure of chromosomes remained largely unexplored. Based on studies using light microscopy, electron microscopy, tomography and mechanical measurements, various models of chromosomes have been proposed. A recent molecular technology Hi-C, introduced in 2009, have enabled studies of chromosomal folding on a genome-wide scale at high resolution, and was since applied to a range of organisms and cell types.
When applied to interphase human cells, Hi-C reveals several levels of compartmentalization of chromosomes. At megabase level, the genome is partitioned into transcribed gene-rich regions, enriched in active chromatin marks (“active” regions), and “inactive” gene-poor regions. At sub-megabase level, chromosomes can be viewed as a sequence of Topologically Associated Domains (TADs), which are 100-800kb regions enriched in chromosomal contacts. Finally, specific looping interactions occur within TADs.
We applied Hi-C to human cells in metaphase, a cell state dedicated to division. We found that mitotic chromosomes assume an entirely different folded state, in which all three levels of interphase chromosome compartmentalization are lost. Moreover, the distribution of chromosomal contact changes: more contacts occur at distances 10Mb, and less at larger distances.
Previously, two fundamental classes of models were proposed for mitotic chromosomes: loops-on-a-scaffold models, where DNA loops are attached to a central axial core structure, and hierarchical models, where chromatin is looped or coiled into increasingly thicker fibers. Using polymer simulations, we found that a loops-on-the-scaffold model of metaphase chromosome folding is consistent with the Hi-C data, and previous microscopy and EM measurements, while hierarchical models are not consistent with Hi-C.
Finally, we propose that mitotic chromosome organization consistent with Hi-C and microscopy data can naturally emerge by a combination of two biological processes: initial linear compaction of the chromosome by formation of an array of consecutive loops, and subsequent axial compression of this loop array to form the final compact mitotic chromosome.
When applied to interphase human cells, Hi-C reveals several levels of compartmentalization of chromosomes. At megabase level, the genome is partitioned into transcribed gene-rich regions, enriched in active chromatin marks (“active” regions), and “inactive” gene-poor regions. At sub-megabase level, chromosomes can be viewed as a sequence of Topologically Associated Domains (TADs), which are 100-800kb regions enriched in chromosomal contacts. Finally, specific looping interactions occur within TADs.
We applied Hi-C to human cells in metaphase, a cell state dedicated to division. We found that mitotic chromosomes assume an entirely different folded state, in which all three levels of interphase chromosome compartmentalization are lost. Moreover, the distribution of chromosomal contact changes: more contacts occur at distances 10Mb, and less at larger distances.
Previously, two fundamental classes of models were proposed for mitotic chromosomes: loops-on-a-scaffold models, where DNA loops are attached to a central axial core structure, and hierarchical models, where chromatin is looped or coiled into increasingly thicker fibers. Using polymer simulations, we found that a loops-on-the-scaffold model of metaphase chromosome folding is consistent with the Hi-C data, and previous microscopy and EM measurements, while hierarchical models are not consistent with Hi-C.
Finally, we propose that mitotic chromosome organization consistent with Hi-C and microscopy data can naturally emerge by a combination of two biological processes: initial linear compaction of the chromosome by formation of an array of consecutive loops, and subsequent axial compression of this loop array to form the final compact mitotic chromosome.
February 12, 2015 (NOTE: Thursday at 2pm!): Mijo Simunovic, University of Chicago and Curie Institute
Reshaping biological membranes: from molecular interactions to macroscopic mechanics
Host: E. Siggia
Biological membranes are fluid and elastic surfaces that change its shape in the course of key cellular phenomena, such as endocytosis, infection, immune response, division, etc. Curvature also controls the way proteins interact with one another and so it acts as a vital signaling mechanism in the cell. Cell’s most notable remodelers are BAR proteins, known to both generate membrane curvature and sense complex membrane morphologies. We combine theoretical modeling with experimental biophysical methods to study the driving force underlying the reshaping of biological membranes induced by BAR proteins. In particular, we employ coarse-grained molecular dynamics and continuum-mechanics simulations to study the assembly of proteins on the membrane and elucidate how their association affects membrane’s large-scale morphology. It also lets us identify a surprising sensitivity of protein-protein attractions on the large-scale mechanics. On the other hand, by using quantitative fluorescence and atomic force microscopies, we investigate the curvature-function relationship of membranes at much larger time and length scales. We study the recruitment of BAR proteins on membrane tubules and the way curvature triggers the formation of rigid protein scaffolds. Finally, we show an unexpected relationship between BAR proteins and molecular motors that drives membrane fission in a newly discovered endocytic mechanism. Our combined theoretical and experimental approach helps explain, at various different resolutions, how BAR proteins regulate membrane dynamics in living cells.
February 17, 2015: (NOTE: at 2pm!) Alex Lang, Boston University
Epigenetic Landscapes Explain Cellular Identity and Reprogramming
Host: C. Kirst
A common metaphor for describing development is a rugged ‘‘epigenetic landscape’’ where cell fates are represented as attracting valleys resulting from a complex regulatory network. I previously introduced a framework for explicitly constructing epigenetic landscapes that combines genomic data with techniques from spin-glass physics such as the projection method Hopfield neural network. This model provides predictions for reprogramming experiments such as requiring the existence of partially reprogrammed cells, novel reprogramming protocols, and the emphasis of this talk, reaction coordinates for dynamics. I will present some intriguing evidence that reprogramming can be accurately described by a rugged barrier crossing.
February 24, 2015: Lawrence Sirovich, The Rockefeller University
A New Structural Approach to Genomic Discovery of Disease: Example of Adult-Onset of Diabetes.
Host: C. Kirst
It will be shown that: (a) Shannon information theory, as extended by Jaynes vastly improves discovery of potential risk loci; (b) an allele, rather than a SNP perspective contains more information, and is computationally superior; (c) a rigorous digitalization of genomic symbol data permits application of powerful tools, leading to a disease classifier that numerically quantifies disease likelihood.
The present methodology, which departs substantially from customary practice, proves fruitful in the investigation of Genome Wide Association Studies (GWAS). It extends naturally and successfully to predicting genomic disposition to disease, arising from large collections of weakly contributing loci.
Strong evidence will be advanced that adult onset diabetes (“type 2 diabetes”, T2D) is such a candidate disease, and specific genes will be implicated. T2D is characterized by a large pool of potential disease loci. When present in sufficient number in someone then that individual is judged to have diabetes. There are virtually limitless different assemblies of the pool which can be designated as diabetes.
The present methodology, which departs substantially from customary practice, proves fruitful in the investigation of Genome Wide Association Studies (GWAS). It extends naturally and successfully to predicting genomic disposition to disease, arising from large collections of weakly contributing loci.
Strong evidence will be advanced that adult onset diabetes (“type 2 diabetes”, T2D) is such a candidate disease, and specific genes will be implicated. T2D is characterized by a large pool of potential disease loci. When present in sufficient number in someone then that individual is judged to have diabetes. There are virtually limitless different assemblies of the pool which can be designated as diabetes.
March 12, 2015 (NOTE: Thursday): Florian Engert, Harvard University
Neural circuits underlying operant learning in larval zebrafish
Host: C. Kirst
During operant conditioning, animals learn to respond to stimuli with actions that lead to favorable outcomes. Recordings in mammals have uncovered neural signals that link stimulus, action, and outcome, but a limited number of recording sites have hindered the comprehensive discovery of learning signals. Here, we use brain-wide functional imaging in larval zebrafish to screen more than 100,000 neurons while animals learn to terminate a heat stimulus with a directional tail movement. We identify neurons comprising two major classes: class 1 consists of action-selective neurons that encode the direction of heat-evoked tail movements seconds before and after their execution. Class 2 consists of neurons that encode outcome prediction and prediction error. This class includes both positive relief prediction signals that are enhanced by learning, and negative relief prediction signals that are suppressed by learning. These positive and negative relief prediction signals not only have opposing patterns of activity but are also found on opposing sides of the habenula. Strikingly, both outcome-predictive and action-selective signals are correlated with natural variability in learning performance across animals. This study provides the first comprehensive survey of the neural dynamics during learning, suggests that lateralized neuronal activity contributes to operant conditioning, and raises novel hypotheses about the roles of action-selective and outcome-prediction neurons.
March 17, 2015: Roy Bar-Ziv, Weizmann Institute of Science and Harvard University
Programmable On-Chip DNA Compartments as ‘Artificial Cells’
Host: C. Kirst
We present 2D DNA compartments fabricated in silicon as ‘artificial cells’ capable of metabolism, programmable protein synthesis, and communication. Metabolism is maintained by continuous diffusion of nutrients and products through a thin capillary, connecting protein synthesis in the DNA compartment with the environment. We programmed protein expression cycles and auto-regulated protein levels in separates ‘cells’. We used a genetic switch to program emergent long-range front propagation of gene expression a 1D array of ‘cells’ with short-range interactions. The propagation velocity is maximal near a saddle-node bifurcation to a monostable homogenous regime, where propagation breaks down. Near the transition expression onset time exhibits slowing down and strong fluctuations. This demonstrates how on-chip gene circuits as dynamical systems can be programmed to drive spatiotemporal patterns and intercellular communication, cellular variability, and spontaneous symmetric breaking.
March 24, 2015: Denis Wirtz, Johns Hopkins University
Cancer Cell Migration in 3D
Host: C. Kirst
Two-dimensional (2D) in vitro culture systems have for a number of years provided a controlled and versatile environment for mechanistic studies of cell adhesion, polarization, and migration, three interrelated cell functions critical to cancer metastasis. However, the organization and functions of the cytoskeleton, focal adhesion proteins, protrusion machinery, and microtubule-based polarization in cells embedded in more physiologically relevant 3D extracellular matrices are qualitatively different from their organization and functions on conventional 2D planar substrates. This talk will describe the implications of the dependence of these fundamental differnces on micro-environmental dimensionality (1D vs. 2D vs.. 3D), how cell micromechanics plays a critical role in promoting local cell invasion, and associated validation in mouse models. We will discuss the implications of this work in cancer metastasis.
March 26, 2015 (Note: Thursday!): Andrew Leifer, Princeton University
Whole-brain neural dynamics and behavior in freely moving nematodes
Host: C. Kirst
How does a nervous system control an animal’s behavior? We are investigating this question by manipulating and monitoring neural activity of populations of neurons in the brain of a simple transparent organism and correlating the observed neural dynamics with behavior. I will present a suite of optical tools to control and record activity in the nematode C. elegans as it moves, including the first instrument to perform whole-brain calcium imaging with cellular resolution in an awake and unrestrained behaving animal. We have used these techniques to gain insight into the underlying neural mechanisms behind mechanosensation, forward locomotion, and the C. elegans escape response. These measurements are a critical first step for investigating neural coding of behavior, decision-making and the time evolution of internal brain states.
April 7, 2015: Joao Xavier, Memorial Sloan Kettering Cancer Center
Evolution and ecology in microbial communities
Host: C. Kirst
Living organisms exist at many scales of biological organization: prokaryotic organisms, eukaryotic unicellular organisms, multicellular organisms and even eusocial organism such as the societies of ants and bees. But what drove the major evolutionary transitions between levels of biological organization? We investigate this problem using a prokaryotic model: swarming in the bacterium Pseudomonas aeruginosa. Swarming is a collective form of motility whereby billions of bacterial cells come together to move across surfaces. It requires the production of rhamnolipid biosurfactants that wet the surface, but biosurfactant synthesis can be costly to individual cells. We use a combination of mathematical modeling and quantitative experiments to investigate how bacteria make the decision to produce biosurfactants by expressing the genes for rhamnolipid synthesis. Beyond P. aeruginosa we aim to uncover general principles of how individual cells implement social decision-making, a problem with relevance to many systems including the evolution of multicellularity and cancer.
April 14, 2015: Adam Cohen, Harvard University
Bringing bioelectricity to light
Host: C. Kirst
Every lipid membrane can support a transmembrane potential, and membrane voltage affects the dynamics of a huge variety of biological processes. However, this quantity has remained difficult to measure. We engineered a microbial rhodopsin protein to function as a fluorescent reporter of membrane voltage. Archaerhodopsin-derived voltage-indicating proteins enable optical mapping of bioelectric phenomena with unprecedented speed and sensitivity. We are developing molecular tools, transgenic animals, instrumentation, and software to visualize dynamic bioelectric effects in canonical systems (neurons, cardiac cells) and in places where bioelectricity is little studied (bacteria, plants, endothelial cells).
April 21, 2015: Arvind Murugan, Harvard University
The tradeoff between control and multi-functionality in disordered frustrated materials
Host: C. Kirst
An attractive feature of bottom-up approaches to creating novel materials is that only minimal control of the system is needed at `run time’. For example, in molecular self-assembly, polymer folding, or actuation of mechanical meta-materials, the microscopic interactions are programmed to be so constraining that only the desired behavior is allowed. The implicit assumption is that such single-purpose microscopic interactions, dedicated to only one behavior, are needed in order to get away with minimal dynamical control of the system.
Instead, we find a non-trivial tradeoff between control and multi-functionality by connecting to ideas in theoretical neuroscience (“associative memory’’). We find that a finite number (“capacity’’) of distinct behaviors can be simultaneously programmed into a system without much incremental need for dynamical control. However, programming any additional behaviors leads to a new regime that requires dramatically finer dynamical control of the system. We illustrate these ideas using models and examples of multi-functional DNA self-assembly, mechanical meta-materials and ribozymes.
Instead, we find a non-trivial tradeoff between control and multi-functionality by connecting to ideas in theoretical neuroscience (“associative memory’’). We find that a finite number (“capacity’’) of distinct behaviors can be simultaneously programmed into a system without much incremental need for dynamical control. However, programming any additional behaviors leads to a new regime that requires dramatically finer dynamical control of the system. We illustrate these ideas using models and examples of multi-functional DNA self-assembly, mechanical meta-materials and ribozymes.
April 28, 2015: Harry Swinney, University of Texas, Austin
Collective dynamics and phenotype switching in bacterial colonies
Host: C. Kirst
We find that Bacillus subtilis bacteria in a growing colony exhibit large (non-thermal) number fluctuations. Motile bacteria within a colony form dynamic clusters defined by nearest neighbors having nearly the same speed and orientation. Surprisingly, the speed and orientational correlations of bacteria within a cluster are scale invariant. Studies of another rod-shaped motile bacterium found commonly in soil, Paenibacillus dendritiformis, reveal that neighboring colonies secrete a toxic protein, Slf, which is not secreted by an isolated colony. Some bacteria within a colony survive Slf but are induced to switch to immotile Slf-resistant cocci. If the cocci encounter sustained favorable conditions, they secrete a signaling molecule that induces a switch back to the rod-shaped form. Genes encoding components of this phenotypic switching pathway are widespread among bacterial species, suggesting that this survival mechanism is not unique to P. dentritiformis.
May 5, 2015: Will Ratcliff, Georgia Tech
Exploring the origin of multicellularity through experimental evolution
Host: C. Kirst
The origin of multicellularity was one of the most significant innovations in the history of life. Our understanding of the evolutionary processes underlying this transition remains limited, however, mainly because extant multicellular lineages are ancient and most transitional forms have been lost to extinction. We bridge this knowledge gap by evolving novel multicellularity in vivo, using baker’s yeast and Chlamydomonas reinhardtii as model systems. In this talk I will cover recent work examining: 1) how cells evolve to form multicellular clusters, 2) how these clusters become ‘Darwinian individuals’ capable of adaptation, 3) how multicellular life cycles that include single-celled genetic bottlenecks arise in evolution (and why this is important), and 4) how nascent multicellular entities evolve to be more complex. Our approach, which allows for the study of macroevolutionary processes over microevolutionary timescales, demonstrates that multicellularity is less evolutionarily constrained than previously thought. If time permits, I will briefly cover ongoing projects in our lab (examining, for example, the origin of multicellular development and the ‘ratcheting hypothesis’ for multicellular complexity).
May 14, 2015 (Note: Thursday!): Ana Hocevar, The Rockefeller University
Multi-Stimulus Scene Representations in the Macaque Face-Processing System
Host: C. Kirst
May 19, 2015: Nachum Ulanovsky, Weizmann Institute of Science
Neural codes for 2-D and 3-D space in the hippocampal formation of bats
Host: C. Kirst
The work in our lab focuses on understanding the neural basis of spatial memory and spatial cognition in freely-moving, freely behaving mammals – employing the echolocating bat as our animal model. I will describe our recent studies, including: (i) Recordings of 3-D place cells, 3-D grid cells, and 3-D head-direction cells in the hippocampal formation of freely-flying bats, using a custom neural telemetry system – which revealed an elaborate 3-D spatial representation system in the bat’s brain; and (ii) Absence of theta oscillations in the bat’s hippocampal formation – arguing against a central role of theta in spatial cognition. I will also describe our recent studies of spatial memory and navigation of bats in the wild, using micro-GPS devices, which revealed outstanding navigational abilities and provided the first evidence for a large-scale ‘cognitive map’ in a mammal.
August 27, 2015 (Note: Thursday 3pm!): Martin Stemmler, LMU Munich
The hexagonal grid code as a multi-dimensional clock for space
Host: C. Kirst
In the hippocampal formation, some neurons fire whenever the animal’s location coincides with a point of an imaginary, hexagonal grid that tessellates space. Such neurons, called grid cells, are thought to play an important role in spatial navigation. Achieving a metric representation for space requires more than a single grid cell; indeed, it requires an ensemble of neurons organized into separate modules, with each module representing a different spatial scale. For the population code to yield a metric, I will show that all grid lattices within a module must share the same orientation, which permits decoding through population vectors. The periodic nature of grid cells makes a simple population vector read-out equivalent to maximal likelihood decoding, which is optimal. Furthermore, the read-out can be made robust to distortions in the regular grid pattern near the boundaries of the environment (Stensola et al. 2015) to permit a global grid-cell metric. Computing the homing vector is least error-prone when the ratio of successive grid scales is around 3/2, consistent with the scales found in the mammalian grid code (Stensola et al, 2013). Silencing intermediate-scale modules should cause systematic errors in navigation, while knocking out the module at the smallest scale will only affect navigational precision. In this scheme, read-out neurons behave like goal-vector cells, for which the goal location depends on nonlinear gain fields, similar to the gain fields found in parietal cortex.
September 17, 2015 (Note: Thursday): Tomaso Poggio, MIT
Visual Cortex and Deep Networks
Host: C. Kirst
I-theory starts from the hypothesis that invariant representations of images are the main computational goal of the ventral stream in visual cortex. Invariant representations can be proved to lead to lower sample complexity in image recognition. We propose a biologically plausible simple-complex cells module (HW module) for computing components of an invariant signature. We extend it to an architecture that in addition to invariance achieve efficient approximation of multidimensional functions by using an extension of additive splines that we call hierarchical additive splines. We show that today’s Deep Convolutional Networks can be characterized in terms of this theoretical framework.
September 22, 2015: György Buzsáki, NYU
Why do we need so many neurons?
Host: C. Kirst
Performance in sensory perception, time and space perception, decision-making, short-term memory and motor control obeys the Weber (log-scale) law. What neuronal mechanisms can support such a wide dynamic range yet in a well-controlled manner? I will demonstrate that lognormal distributions are fundamental to both structural and functional brain organization. A small minority of fast firing neurons and connections may be responsible for ‘good enough’ brain performance but deployment of the weakly active majority is needed for precision performance. I will illustrate these speculative ideas with concrete examples.
September 24, 2015 (Note: Thursday): Ilya Nemenman, Emory
Coarse-grained models in systems biology and computational neuroscience
Host: C. Kirst
Even though this goal is often professed, one cannot hope to have a “complete” model of any biological system, which would enumerate and quantitatively account for every possible bio-molecular player. Explicit coarse-grained models generalize better, and bring about more understanding. Here I will explore a few examples of coarse-grained models in cellular and neural systems, and will show some unexpected findings that this modeling approach uncovered.
September 29, 2015: Jonathan Pillow, Princeton University
Unlocking single-trial dynamics in parietal cortex during decision-making
Host: C. Kirst
Neural firing rates in the macaque lateral intraparietal (LIP) cortex exhibit gradual “ramping” that is commonly believed to reflect the accumulation of sensory evidence during decision-making. However, ramping that appears in trial-averaged responses does not necessarily indicate that the spike rate ramps on single trials; a ramping average rate could also arise from instantaneous steps that occur at different times on each trial. In this talk, I will describe an approach to this problem based on explicit statistical latent-dynamical models of spike trains. We analyzed LIP spike responses using spike train models with: (1) ramping “accumulation-to-bound” dynamics; and (2) discrete “stepping” or “switching” dynamics. Surprisingly, we found that three quarters of choice-selective neurons in LIP are better explained by a model with stepping dynamics. We show that the stepping model provides an accurate description of LIP spike trains, allows for accurate decoding of decisions, and reveals latent structure that is hidden by conventional stimulus-aligned analyses.
October 6, 2015: Benjamin Machta, Princeton University
Do anesthetics act through a membrane critical point?
Host: C. Kirst
Many small molecules induce general anesthesia in animals ranging from insects to mammals. Although their mechanism of action remains controversial, it is known that a compound’s efficacy as an anesthetic is strongly predicted by both its hydrophobicity and its potency in inhibiting a wide variety of ligand gated ion channels. Here I will argue that anesthetics interfere with and mimic native regulation of ion channels by their two-dimensional solvent, the plasma membrane, which is tuned close to a liquid-liquid critical point. I will first review the evidence suggesting that plasma membranes are indeed tuned close to such a de-mixing critical point. I will then report on recent experiments where we show that the n-alcohol anesthetics take plasma membrane derived vesicles away from criticality by lowering their critical temperature. I will then discuss our model for understanding anesthetic effects, in which the nearly critical membrane’s order parameter acts as an allosteric modulator for bound channels. I will show simulation results from the 2D-Ising model that suggest that ion channels could indeed be strongly affected by our measured changes in critical temperatures. Our mechanistic model makes a wide array of predictions that we are beginning to test. I will present our preliminary results showing that several compounds that reverse anesthetic effects on membrane criticality also reverse tadpole anesthesia and functional effects at the channel level. In addition, we predict that channels regulated in this way will have their partitioning into membrane domains altered by function, both in intact cells and in cell-derived vesicles.
October 13, 2015: Weiji Ma, NYU
Probabilistic inference by humans, monkeys, and neural networks
Host: C. Kirst
I will describe: – the “top-down” approach of understanding brain function by starting from behavior; – probabilistic inference as a framework to qualitatively account for perceptual illusions; – probabilistic inference as a framework to quantitatively account for decision-making behavior in laboratory tasks; – two possible neural implementations of probabilistic inference in a decision-making task.
October 14, 2015 (Note: Wednesday 2pm!): Omri Barak, Technion
Understanding trained recurrent neural networks
Host: C. Kirst
Recurrent neural networks are an important class of models for explaining neural computations. Recently, there has been progress both in training these networks to perform various tasks, and in relating their activity to that recorded in the brain. Despite this progress, there are many fundamental gaps towards a theory of these networks. Neither the conditions for successful learning, nor the dynamics of trained networks are fully understood. I will present a detailed analysis of very simple tasks as an approach to build a theory of general trained recurrent neural networks.
October 20, 2015: Daan Frenkel, University of Cambridge
Entropy and addressable self-asssembly
Host: P. Sulc
A holy grail of nano-technology is to create truly complex, multi-component structures by self assembly. Most self-assembly has focused on the creation of `structural complexity’. In my talk, I will discuss `Addressable Complexity’: the creation of structures that contain hundreds or thousands of distinct building blocks that all have to find their place in a 3D structure. Recent experiments have demonstrated the feasibility of making such structures. Simulation and theory yield surprising insights that can inform the design of novel structures and materials.
November 3, 2015: Jasna Brujic, NYU
Biomimetic Emulsions As Models of Cellular Aggregates
Host: P. Sulc
The self-assembly of cells into 3D structures, such as biological tissues, is important during embryogenesis, morphogenesis and wound healing. Inspired by biological systems, we design and make droplets stabilized by lipid mixtures, which are functionalized with cell-cell adhesion proteins. We discover that lipids phase separate on the droplet surface to create stable and tunable patterns of circular or stripy domains, reminiscent of lipid rafts in cell membranes. These domains carry adhesive proteins, which then drive the specific and reversible binding between droplets to generate large scale structures. For example, we show that these mobile adhesion patches can self-assemble linear chains of droplets into compact structures. These results demonstrate the control of valency, geometry and specificity of droplet bonds to open novel routes to the self-assembly of biocompatible scaffolds for the controlled study of 3D migration of biological cells.
November 10, 2015: Andre Longtin, University of Ottawa
Dynamical encoding of looming, receding, and focussing.
Host: C. Kirst
This talk will discuss a non-conventional neural coding task that may apply more broadly to many senses in higher vertebrates. We ask whether and how a non-visual sensory system can focus on an object. We present recent experimental and modeling work that shows how the electric sense can perform such neuronal focussing. This sense is the main one used by weakly electric fish to navigate, locate prey and communicate in the murky waters of their natural habitat. We show that there is a distance at which the Fisher information of a neuron’s response to a looming and receding object is maximized, and that this distance corresponds to a behaviourally relevant one chosen by these animals. Strikingly, this maximum occurs at a bifurcation between tonic firing and bursting. We further discuss how the invariance of this distance to signal attributes can arise, a process that first involves power-law spike frequency adaptation. The talk will also highlight the importance of expanding the classic dual neural encoding of contrast using ON and OFF cells in the context of looming and receding stimuli.
November 12, 2015: (Note: Thursday): Richard Neher, Max Planck Institute for Developmental Biology
Rapid adaptation and the predictability of evolution
Host: C. Kirst
Evolution is simple if adaptive mutations appear one at a time. However, in large microbial populations many mutations arise simultaneously resulting in a complex dynamics of competing variants. I will discuss recent insight into universal properties of such rapidly adapting populations and compare model predictions to whole genome deep sequencing data of HIV-1 populations at many consecutive time points. Genetic diversity data can further be used to infer fitness of individuals in a population sample and predict successful genotypes. We validate these prediction using historical influenza virus sequence data. Successful predictions of the composition of future influenza virus population could guide strain selection for seasonal influenza vaccines.
November 17, 2015: Larry Abbott, Columbia University
Random Representations in Neural Circuits
Host: C. Kirst
Wiring in neural circuits ranges from highly stereotyped to apparently random. Using examples of random circuits in flies, fish and mice, I will discuss the logic behind the design and function of random representations in sensory processing, learning and decision making.
November 19, 2015 (Note: Thursday): Misha Ahrens, Janelia Research Campus
Neural circuit discovery through large-scale imaging in behaving zebrafish
Host: C. Kirst
Many important behaviors arise from the joint activity of large numbers neurons spread over multiple brain areas. We use a combination of light-sheet microscopy and a virtual reality setup to image activity in almost the entire brain (~100k neurons) of fictively behaving larval zebrafish. Our general approach is to evoke the relevant behavior in the VR setup, record whole-brain activity optically, apply large-scale computational techniques to create functional maps, and use these to guide perturbation experiments and anatomical mapping. We first addressed how structure in spontaneous behavior arises from brain activity. We analyzed the spatiotemporal structure of spontaneous swim trajectories in the absence of any salient sensory cues, in both freely swimming and virtual reality settings. From whole-brain functional maps we identified a relatively small number of neurons in stereotyped locations, which through lesion and stimulation experiments we showed to be causally related to the spontaneous behavior, critical to structuring swim trajectories. Using tracing techniques, we found putative connections between the bias-inducing neurons and premotor neurons that can drive turning behavior, leading to an integrative model of how this circuit controls spatiotemporal patterning of spontaneous locomotion. We also use similar techniques to analyze neural circuitry underlying motor learning and motor memory formation. These studies demonstrate how whole-brain imaging during behavior can localize and map the dynamics of previously unknown functional populations of neurons.
December 1, 2015: Timothy Buschman, Princeton University
Neural Dynamics of Cognitive Flexibility
Host: C. Kirst
Cognitive flexibility allows us to quickly change our behavior to fit the situation. This is thought to require the dynamic re-mapping of neural circuits in order to select contextually appropriate behaviors. Here I present evidence that changes in synchrony within and between brain regions may underlie such dynamic re-mapping. Leveraging large-scale, multiple-region electrophysiology in non-human primates, I will show different patterns of synchrony in frontal and parietal cortex support different behaviors. In particular, different tasks are encoded by distinct dynamic, synchronous, sub-networks within prefrontal cortex and that these sub-networks organize the spiking activity of single neurons carrying task-relevant information. Such synchronous sub-networks may provide an ideal mechanism for flexibly associating task-relevant information, supporting cognitive flexibility.
December 8, 2015: Marija Vucelja, University of Virginia
Physicists take on the adaptive immune system of bacteria aka CRISPR
Host: C. Kirst
In this talk, I am going to review a bit of CRISPR biology, which inspired my colleagues and me to study this fascinating immune system of bacteria. The CRISPR (clustered regularly interspaced short palindromic repeats) mechanism allows bacteria to defend adaptively against phages and other invading genomic material. The CRISPR machinery acquires short genomic sequences (so-called spacers) from the “invaders” and in this way builds up a memory of past infections. With a new encounter of an invading sequence, this memory is accessed, and in a successful outcome the invader is neutralized. I will introduce a population dynamics model where immunity can be both acquired and lost. Two key parameters of the model are the ease of acquisition and the spacer effectiveness in conferring immunity. I will describe the predictions of this model and suggest potential experiments.
December 10, 2015 (Note Thursday): E.J. Chichilnisky, Stanford University
Deciphering neural computation and circuitry in the retina at cellular resolution
Host: C. Kirst
A central challenge in neuroscience is to understand connectivity and computations in neural circuits. In the retina, diverse cell types process visual information captured by photoreceptors with exquisite precision and specificity, delivering multiple distinct visual images to the brain. I will present the approach we have taken to understanding retinal computations using large-scale electrical recordings combined with high-resolution visual stimulation. First, I will describe the unique technical approach we have developed for this purpose and the foundational work in the field on which it is based, which will likely be novel to a physics audience. Then, I will show how our large-scale, high-resolution measurements first allowed us to reveal functional connectivity in the retina at cellular resolution and circuit scale a few years ago. Finally, I will show how we have recently extended this approach to decipher one of the elementary nonlinear neural computations that the retina performs on the visual scene.
Jan. 14, 2014: Matthieu Wyart, New York University
The Memory of Sand
Host: A. Hocevar
Complex systems are characterized by an abundance of meta-stable states. To describe such systems statistically, one must understand how states are sampled, a difficult task when thermal equilibrium does not apply. This problem arises in various fields of science, and here I will focus on a simple example, sand. Sand can flow until one jammed configuration (among the exponentially many possible ones) is reached. I will argue that these dynamically-accessible configurations are atypical, implying that in its solid phase sand “remembers” that it was flowing just before it jammed. As a consequence, it is stable, but barely so. I will argue that this marginal stability answers long-standing questions both on the solids and liquid phase of granular materials, and will discuss the applicability of this idea to other systems.
Jan. 21, 2014: Michael Romalis, Princeton University
Atomic magnetometers for magnetoencephalography
Host: A. Hocevar
Magnetoencephalography is one of the leading techniques for functional brain studies. So far it has relied on arrays of cryogenic SQUID magnetometers. I will describe a new technique using atomic magnetometers that brings additional capabilities for MEG studies. We have developed a new type of optically-pumped magnetic field sensors that can compete with SQUIDs in many applications. They do not require cryogenic cooling and allow greater flexibility of sensor placement and as well as higher magnetic field sensitivity. Several groups worldwide have been pursuing MEG using these atomic sensors with promising initial results. I will also describe another type of atomic magnetic field sensor that can convert magnetic field magnitude directly to frequency measurements, allowing very high dynamic range and noise rejection. Such sensors recently reached sensitivity competitive with SQUIDs and may allow, for the first time, MEG recordings in magnetically-unshielded environment.
Jan. 28, 2014 (2PM – Physics Fellow Candidate): Petr Sulc, University of Oxford
Coarse-grained modelling of nucleic acids
Host: E. Siggia
Feb. 4, 2014 (2PM – Physics Fellow Candidate): Mikhail Tikhonov, Princeton University
Exploiting dynamical metagenomics to study microbial communities
Host: E. Siggia
Feb. 11, 2014 (2PM – Physics Fellow Candidate): Dmitry Krotov, Princeton University
Criticality in transcriptional networks
Host: E. Siggia
Feb. 18, 2014 (2PM – Physics Fellow Candidate): Edouard Hannezo, Institut Curie
Biophysical modeling of epithelial tissues
Host: E. Siggia
Feb. 25, 2014: Raul Rabadan, Columbia University
The Topology of Evolution
Host: A. Hocevar
The tree structure is currently the accepted paradigm to represent evolutionary relationships between organisms, species or other taxa. However, horizontal, or reticulate, genomic exchanges are pervasive in nature and confound characterization of phylogenetic trees. Drawing from algebraic topology, we present a unique evolutionary framework that comprehensively captures both clonal and reticulate evolution. We show that whereas clonal evolution can be summarized as a tree, reticulate evolution exhibits nontrivial topology of dimension greater than zero. Our method effectively characterizes clonal evolution, reassortment, and recombination in RNA viruses. Beyond detecting reticulate evolution, we succinctly recapitulate the history of complex genetic exchanges involving more than two parental strains, such as the triple reassortment of H7N9 avian influenza and the formation of circulating HIV-1 recombinants. In addition, we identify recurrent, large-scale patterns of reticulate evolution, including frequent PB2-PB1-PA-NP cosegregation during avian influenza reassortment. Finally, we bound the rate of reticulate events (i.e., 20 reassortments per year in avian influenza). Our method provides an evolutionary perspective that not only captures reticulate events precluding phylogeny, but also indicates the evolutionary scales where phylogenetic inference could be accurate.
Mar. 4, 2014: cancelled! Christopher Henley, Cornell University – cancelled
TBD
Host: A. Hocevar
Mar. 6, 2014: Gasper Tkacik, Institute of Science and Technology Austria
Beyond sensory bottleneck: Efficient coding of elements of visual form
Host: A. Hocevar
It has long been appreciated that the statistical properties of natural stimuli shape neural processing mechanisms in the sensory periphery, but the extent to which such a principle can be formulated for and applied to central processing is unclear. The periphery faces a transmission bottleneck, so efficiency implies compression of signal components with a predictably wider range. Cortex faces a different challenge – it must use limited samples to make inferences to guide decisions. In this regime, efficient coding predicts the opposite from the periphery: that greater resources are allocated to the signal components with a wider range. To test this hypothesis, we carry out two parallel studies. In one, we measure the joint distribution of local two-, three-, and four-point spatial correlations in an ensemble of natural images. In the other, we measure human perceptual sensitivity to these correlations and their combinations via psychophysical experiments that use synthetic visual textures. We show that psychophysical performance, described by dozens of independent parameters, can be predicted with surprising accuracy from the distribution of spatial correlations found in the natural images. Thus, the efficient coding principle extends beyond the sensory periphery to the central nervous system, where it applies in a very different guise and accounts for the sensitivity to higher-order elements of visual form.
Mar. 11, 2014: Jane Kondev, Brandeis University
Action at a distance in the yeast nucleus
Host: A. Hocevar
Various functions performed by chromosomes involve long-range communication between DNA sequences that are tens of thousands of bases apart. In this talk I will discuss experiments and theory relating to two distinct modes of long-range communication, chromosome looping and hopping, both in the context of DNA break repair in yeast. In particular, I will show that loss of polymer entropy due to chromosome looping serves as the driving force for homology search during DNA break repair. Also, I will discuss the spread of histone modifications away from the DNA break point in the context of a simple physics models based on a kinase hopping along the chromosome. These examples reveal physical principles of long-range communication in the nucleus, and how they can be tested experimentally.
Mar. 18, 2014: Mark Goulian, University of Pennsylvania
Bacterial signal transduction. An E. coli view of the world.
Host: A. Hocevar
Cells sense and respond to physical and chemical cues in their environments through networks of interacting proteins–signal transduction systems–that detect and interpret specific inputs, and control the appropriate physiological responses. In bacteria, one of the major modes of signal transduction is mediated by a class of circuits that are composed of two proteins: a sensor kinase and a response regulator. These two-component systems have been found in remarkable numbers within individual organisms and across different bacterial species. They play a central role in regulating basic aspects of microbial physiology and mediate adaptation to diverse environments. I will describe work in which we have explored the organization and properties of these circuits in E. coli.
Apr. 1, 2014: Nicolas Buchler, Duke University
Invade, co-opt, and swap: Evolution of G1/S cell cycle control in Fungi and other eukaryotes
Host: A. Hocevar
The core eukaryotic cell cycle is believed to have emerged before the last common ancestor of plants, fungi, and animals. The structure of the G1–S regulatory network and its dynamic properties are highly similar in budding yeast and mammals despite lack of sequence homology between many G1/S regulators (cyclins, transcription factors, and inhibitors). This is all the more striking because plant and animal G1/S regulators (Cyclin D, E2F/DP, Rb) have much higher sequence homology even though fungi and animals are more closely related.
To investigate cell cycle evolution, we constructed a eukaryotic phylogeny of the G1/S cyclin, cyclin-dependent kinase, inhibitor, and transcription factor super-families. We show that the G1/S regulatory network present in plants and animals was present in the last common eukaryotic ancestor, and that a fungal ancestor evolved novel G1/S cyclins (CLN), inhibitor (Whi5), and transcription factors (SBF/MBF). The fungal G1/S cyclins (CLN) co-cluster with B-type cyclins found in plants and animals. Basal Fungi are characterized by the emergence of a novel Whi5 inhibitor and novel SBF/MBF transcription factors followed by the eventual loss of the ancestral Rb inhibitor and E2F/DP transcription factors. Strikingly, SBF/MBF DNA-binding domain has sequence homology to the KilA-N domain, which suggests a viral origin for this fungal-specific family of transcription factors. It appears that these new regulators were able to acquire, maintain, and eventually usurp the function and dynamics of the original G1-S regulatory network following the formation of a hybrid network. The emergence of this hybrid SBF-E2F cell cycle coincides with the emergence of Fungi.
To investigate cell cycle evolution, we constructed a eukaryotic phylogeny of the G1/S cyclin, cyclin-dependent kinase, inhibitor, and transcription factor super-families. We show that the G1/S regulatory network present in plants and animals was present in the last common eukaryotic ancestor, and that a fungal ancestor evolved novel G1/S cyclins (CLN), inhibitor (Whi5), and transcription factors (SBF/MBF). The fungal G1/S cyclins (CLN) co-cluster with B-type cyclins found in plants and animals. Basal Fungi are characterized by the emergence of a novel Whi5 inhibitor and novel SBF/MBF transcription factors followed by the eventual loss of the ancestral Rb inhibitor and E2F/DP transcription factors. Strikingly, SBF/MBF DNA-binding domain has sequence homology to the KilA-N domain, which suggests a viral origin for this fungal-specific family of transcription factors. It appears that these new regulators were able to acquire, maintain, and eventually usurp the function and dynamics of the original G1-S regulatory network following the formation of a hybrid network. The emergence of this hybrid SBF-E2F cell cycle coincides with the emergence of Fungi.
Apr. 8, 2014: Richard Bonneau, New York University
New methods for learning dynamic regulatory network models with priors on network structure: what works in B. subtilis works in mouse (?)
Host: E. Siggia
The identifiability problem is critical problem in the inference of biological networks from genomics data. Thus, even very large observational data-sets are not sufficient for discriminating between a very large number of equally likely network models. Even as the size and quality of expression, proteomics and phospho-proteomics data-sets increases the quality and completeness of large scale regulatory network models derived from these data-sets might not increase. One way to approach this problem is by collecting data-sets that generate priors on network structure (such as ChIP-seq, ATAC-seq, and others). I will discuss new computational methods for using priors in network structure to learn dynamic regulatory network models. These methods use priors on network structure in two ways: 1) to estimate latent activities of transcription and chromatin remodeling factors and 2) to constrain the model selection step of our method for learning dynamic regulatory network models, the Inferelator. Examples application to B. subtilis (with Patrick Eichenberger) and the mouse immune system (with Dan Littman) will be discussed. Lastly, implications for optimal experimental design of large genomics data-collection efforts will be discussed.
Apr. 15, 2014: Rémi Monasson, Laboratoire de Physique Théorique de l’ENS
Inferred Model of the Prefrontal Cortex Activity Unveils Task-Related Cell Assemblies and Memory Replay
Host: M. Vucelja
Cell assemblies are thought to be the units of information representation in the brain, yet their detection from experimental data is arduous. We propose to infer the effective network structure from simultaneously recorded neurons in prefrontal cortex using an inverse Ising model. We define cell assemblies as the co-activated neurons in the dynamics of the resulting abstract neural network. Different dynamical regimes, as may be observed across wakefulness and sleep, can be reproduced by changing a global input parameter, providing access to those rare activity configurations that are crucial for cell assembly replay. The identified assemblies strongly co-activate during wakeful experience and are found to replay during subsequent sleep, in correspondence to the potentiation of the inferred network. Across sessions, a variety of different network scenarios is observed, providing insight in cell assembly formation and replay.
Apr. 17, 2014: (NOTE: Thursday, 2pm!) Bert Kappen, Radboud University Nijmegen, the Netherlands
A statistical physics perspective of control theory
Host: M. Vucelja
Intelligent systems, whether natural or artificial, must act in a world that is highly unpredictable. It is intuitively clear, that an optimal approach to decision making or planning under such circumstances requires to take these uncertainties into account. However, the optimal control solution is intractable to compute in general and in addition is hard to represent, due the non-trivial state dependence of the optimal control. This has prevented large scale application of stochastic optimal control theory sofar. The path integral control theory describes a class of control problems whose solution can be computed as an inference computation in a probability model. This link between control and inference may provide an opportunity to treat learning and acting within the same theoretical framework. In this talk, I will review the theory and discuss the relation to statistical physics. I will then discuss how Monte Carlo importance sampling provides an elegant approach to learning motor control.
Apr. 22, 2014: CANCELLED! Thomas J. Near, Yale University
Climate change and biodiversity of the Southern Ocean
Host: M. Vucelja
The fish fauna of the Southern Ocean is unlike that of any other set of near-shore habitats on Earth as it is completely dominated by a lineage of closely related species: the notothenioids. Over the course of approximately 35 million years, notothenioid fishes have acclimated to the changing environmental conditions of the Southern Ocean and now have a suite of adaptations to avoid freezing in the subzero waters surrounding Antarctica. In this talk, I explore the idea that the ecological and morphological diversity exhibited in Antarctic notothenioids is facilitated by the “key innovation” of anti-freeze glyco proteins (AFGPs). My analyses utilize hypotheses of phylogenetic relationships and estimates of divergence times among notothenioids to reconstruct the history of their diversification, and place key events in the evolutionary history of notothenioids with our understanding of the paleoclimate of Antarctica.