The rockefeller university

Biological systems orchestrate numerous components through complex network interactions, allowing them to function cohesively. Cells use regulatory networks to control their internal states in response to environmental stimuli and signals. In multicellular organisms, cells are organized into spatial structures with diverse signaling pathways and physical interactions that facilitate adaptation and survival.

Jiang’s research focuses on how cell states are defined and regulated by these networks, as well as how they develop their control strategies. By leveraging techniques from statistical mechanics, statistical inference, and machine learning, Jiang has developed computational frameworks to build probabilistic models of regulatory networks using large-scale single-cell perturbation profiling. Jiang employs these models to study cell state transition processes, such as embryonic development and aging, to better understand the transition dynamics, identify key regulators, and design optimal interventions for state control.