Living organisms need to obtain and process information accurately, which is 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), which are driven out of equilibrium. These biochemical and neural circuits 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? Is there a fundamental limit for its performance of the biological functions? What is the optimal design for achieving these information processing tasks? 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 [1,2]; biochemical oscillation for accurate timekeeping [3,4].
 “The energy-speed-accuracy trade-off in sensory adaptation”, G. Lan, P. Sartori, S. Neumann, V. Sourjik, and Yuhai Tu, Nature Physics 8, 422-428, 2012.
 “Free energy cost of reducing noise while maintaining a high sensitivity”, Pablo Sartori and Yuhai Tu, Phys. Rev. Lett. 2015. 115: 118102.
 “The free-energy cost of accurate biochemical oscillations”, Y. Cao, H. Wang, Q. Ouyang, and Yuhai Tu, Nature Physics 11, 772, 2015.
 “Design principles for enhancing phase sensitivity and suppressing phase fluctuations simultaneously in biochemical oscillatory systems”, C. Fei, Y. Cao, Q. Ouyang, and Yuhai Tu, Nature Communications, 2018.
Yuhai Tu received his PhD in Physics from UCSD in 1991. He was a Division Prize Fellow at Caltech from 91-94. He joined IBM Watson Research Center in 1994 and was head of the Theory group 2003-2015. He has been an APS Fellow since 2004. He was chair of the APS Division of Biological Physics (DBIO) in 2017. He has done pioneering work in statistical physics and biophysics, including pattern formation in fluids, collective phenomenon in living systems (e.g., flocking), interface physics, statistical analysis of gene expression, and bacterial chemotaxis. Recently, he has also worked on nonequilibrium physics in living systems, and neuroscience.