报告题目：Modelling and Analysis of Non-Markovian Biochemical Reaction Networks

主 讲 人：张 家 军

单 位：中山大学

时 间：12月10日19:30

腾 讯 ID：278 377 887

摘 要：

Modeling intracellular processes has 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 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 for the fast evaluation of fluctuations. These formulations can have broad applications and may help us discover new biological knowledge.

简 介:

张家军，中山大学数学学院副教授，博士研究生导师。主要从事计算系统生物学和生物信息学研究，在国际重要学术期刊如PNAS、Physical Review Letters、PLoS Computational Biology、Biophysical Journal和SIAM Journal on Applied Mathematics等发表多篇研究论文。主持国家自然科学基金面上项目、国家自然科学青年科学基金项目、广州市珠江科技新星专项等多项科研项目，并作为主要参与人（前三）参与2项国家自然科学重点项目等科研项目。现任中国工业与应用数学学会数员工命科学专业委员会副秘书长、广东省计算数学学会副理事长、中国细胞生物学学会功能基因组信息学与系统生物学分会理事、广东省工业与应用数学学会理事、广东省高性能计算学会理事。获2020世界华人数学家联盟最佳论文奖（若琳奖）。