Speaker: Yuhai Tu, Ph.D., IBM T. J. Watson Research Center
Topic: Nonequilibrium Physics for Living Systems
Complex systems with many degrees of freedom and/or many interacting units are ubiquitous in nature and in artificial systems ranging from a flock of birds to living cells to artificial neural networks. These complex systems exhibit fascinating collective behaviors (e.g., flocking of bird, swarming of bacterial cells); carry out essential biological functions (e.g., sensory adaptation, motility, and biological oscillations for time keeping); and perform at or near human level memory and learning tasks (e.g., associative memory and deep learning). However, almost all of these complex systems operate far out of equilibrium in which equilibrium statistical mechanics fails to describe even their steady state behaviors. In the past 15 years, our research focus has been on developing a theoretical framework for these highly nonequilibrium systems in active matter, living cells, and artificial neural networks to understand dynamics and functions of these complex systems. In this talk, we will present some of our most recent work on deciphering molecular mechanisms underlying various biological systems including signal transduction in receptor-kinase complexes and the circadian clock in Cyanobacteria  by combining experimental data (structural as well as functional data) and theoretical approach based on nonequilibrium statistical physics.