时间：2019年12月16日（周一）09：00-10：00

地点：工业中心506室

报告题目：Modelling and anslysis of non-Markovian intracellular reaction networks

报告人简介: 周天寿，男，教授，数学研究所所长，广东省计算数学重点实验室副主任，中国工业与应用数学学会数员工命科学专业委员会副主任，中国运筹学会计算系统生物学专业委员会副主任。研究方向：计算系统生物学。在SCI刊物发表150余篇论文，包括国际顶尖刊物PNAS和PRL，其中发表在PRL上的论文被Harvard老员工物系M. Springer和J. Paulsson教授在Nature上作了专门评论。在科学出版社出版学术专著2部。曾获全国优秀博士学位论文奖和国家自然科学二等奖。主持4项国家自然科学基金委重点项目。

 内容摘要：Modeling and analysis of intracellular processes have long relied on the Markovian assumption. However, as soon as a reactant interacts with its environment, molecular memory definitely exists and its effects cannot be neglected. Since the Markov theory cannot translate directly to modeling and analysis of non-Markovian processes, this leads to many significant challenges. We develop a novel formulation, namely the stationary generalized chemical master equation, to model intracellular processes with molecular memory. This formulation converts a non-Markovian question to a Markovian one while keeping the stationary probabilistic behavior unchanged. Both a stationary generalized Fokker-Planck equation and a generalized linear noise approximation are further developed, each convenient for the fast evaluation of fluctuations. These formulations can have broad applications and may help us discovery new biological knowledge.

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