The Rockefeller University

January 14, 2025: (4PM)- Ned Wingreen, Princeton University

Capillary Attraction Underlies Bacterial Collective Dynamics. “Water Is The Driving Force Of All Nature.” — Leonardo da Vinci.

Host: Eric Siggia

Collective motion of active matter occurs in many living systems, such as bacterial communities, epithelial cell populations, bird flocks, and fish schools. A remarkable example can be found in the soil-dwelling bacterium Myxococcus xanthus. Key to the life cycle of M. xanthus cells is the formation of collective groups: they feed on prey in swarms and aggregate upon starvation. However, the physical mechanisms that keep M. xanthus cells together remains unclear. I’ll present a computational model to explore the role that capillary forces play in bacterial collective dynamics. The modeling results, combined with experiments, show that water menisci forming around bacteria mediate strong capillary attraction between cells. The model accounts for a variety of previously observed phases of collective dynamics as the result of a competition between cell-cell capillary attraction and cell motility. Finally, I’ll discuss the large-scale self-organization of bacterial populations and highlight the importance of capillary force in this process. Together, these results suggest that cell-cell capillary attraction provides a generic mechanism underpinning bacterial collective dynamics.

January 21, 2025: (4PM)

To Be Announced.

Host: TBD

To come.

January 28, 2025: (4PM)- Jason Kim, Cornell University

Generating Interpretable, Reliable, And Quantitative Models Of Emergent Behavior From High-Dimensional Data.

Host: Eric Siggia

Natural systems with emergent behaviors often organize along nonlinear low-dimensional subsets of high-dimensional spaces. For example, despite the tens of thousands of genes in the human genome, the principled study of genomics is fruitful because biological processes rely on coordinated organization along lower dimensional subspaces of phenotypes. To uncover this organization, many dimensionality reduction techniques embed high-dimensional data in low-dimensional spaces by modeling local relationships between data points. However, these methods fail to directly model the subspaces in which the data reside, thereby limiting their ability to infer the biological processes that globally organize the data, and to generalize out-of-distribution. Here, we address this limitation by directly learning a nonlinear subspace that is well-behaved not only in regions where there are data, but also in regions where there are no data by regularizing the curvature of manifolds generated by autoencoders, a method we coin “Γ-autoencoder.” We demonstrate its utility in a wide range of datasets, including bulk RNA-seq from healthy and cancer tissues, single-cell RNA-seq from cell differentiation, and neural activity from the mouse hippocampus. We discover the global biological programs that emerge as relevant variables, demonstrate superior predictions on data from completely unseen out-of-distribution classes, and consistently learn the same nonlinear subspaces across different random initializations. Broadly, we anticipate that direct modeling of the low-dimensional subspaces that generate and organize data through regularizing the curvature of generative models will enable more interpretable, generalizable, and consistent models in any high-dimensional system with emergent low-dimensional behavior.