北京大学高端学术讲学计划
北京大学定量生物学中心/生命科学联合中心
学术报告
题 目: Nonequilibrium Physics for Living Systems: An Update
报告人: Professor Yuhai Tu
AAAS Fellow, APS Fellow, Chair of the APS Division of Biophysics (DBIO)
IBM Thomas J. Watson Research Center, Yorktown Heights, NY USA
时 间: 10月24日（周二）13:00-14:00
地 点: 吕志和楼B101
主持人: 来鲁华 教授
摘要:
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 [1] and the circadian clock in Cyanobacteria [2] by combining experimental data (structural as well as functional data) and theoretical approach based on nonequilibrium statistical physics.
[1] “A nonequilibrium allosteric model for receptor-kinase complexes: The role of energy dissipation in chemotaxis signaling”, David Hathcock, Qiwei Yu, Bernardo A. Mello, Divya N. Amind, Gerald L. Hazelbauer, Yuhai Tu, PNAS, 2023.
[2] “Determining subunit-subunit interaction from statistics of cryo-EM images: observation of nearest-neighbor coupling in a circadian clock protein complex”, Xu Han, Dongliang Zhang, Lu Hong, Daqi Yu, Zhaolong Wu, Tian Yang, Michael Rust, Yuhai Tu, Qi Ouyang, Nature Communications, 2023.
Professor Yuhai Tu graduated from University of Science and Technology of China in 1987. He came to the US under the CUSPEA program and received his PhD in physics from UCSD in 1991. He was a Division Prize Fellow at Caltech from 1991-1994. He joined IBM Watson Research Center as a Research Staff Member in 1994 and served as head of the theory group during 2003-2015. He has been an APS Fellow since 2004 and served as the APS Division of Biophysics (DBIO) Chair in 2017. He is also a Fellow of AAAS.
Yuhai Tu has broad research interests, which include nonequilibrium statistical physics, biological physics, theoretical neuroscience, and most recently theoretical foundations of deep learning. He has made seminal contributions in diverse areas including the flocking theory, growth dynamics of Si-aSiO2 interface, pattern discovery in RNA microarray analysis, quantitative models of bacterial chemotaxis, circadium clock, and the energy-speed-accuracy relation in biological systems.
For his work in theoretical statistical physics, he was awarded (together with John Toner and Tamas Vicsek) the 2020 Lars Onsager Prize from APS: "For seminal work on the theory of flocking that marked the birth and contributed greatly to the development of the field of active matter." https://www.aps.org/programs/honors/prizes/prizerecipient.cfm?last_nm=Tu&first_nm=Yuhai&year=2020
