Abstract: Living systems from biochemical networks to neural networks are responsible for carrying out precise biological functions and information processing tasks. However, most of these complex networks operate far out of equilibrium in which equilibrium statistical mechanics fails to describe even their steady state properties. It thus remains a major challenge in biological physics to develop a theoretical framework for studying these highly nonequilibrium complex systems.
A central problem in living or active systems is how they manage to perform vital functions (e.g., replication, development, computing, etc.) accurately by using highly noisy information. What are the mechanisms to control noise for accurate information processing? What is the energy cost for implementing these mechanisms? What are the design principles for achieving these biological functions efficiently? In the past 15 years, we have been working to answer these questions in various biochemical systems including ultra-sensitive switch, sensory adaptation, accurate biochemical oscillations, and collective behaviors (e.g., synchronization, flocking, and pattern formation) by using tools and concepts from nonequilibrium statistical physics.
In this talk, we will first describe the general background on the topic followed by presenting some of our most recent work related to the energy cost for collective behaviors in the context of synchronization of molecular clocks and Turing patterns.