报告题目：Approximate method for stochastic chemical kinetics with two-time scales by chemical Langevin equations
报告专家：吴付科教授（华中科技大学）
报告时间：2026年3月29日20:30-21:30
报告地点：复杂系统研究所4层报告厅（商学楼409室）
专家介绍：吴付科，华中科技大学数学与统计学院教授，博士生导师，国家优秀青年基金获得者，入选教育部新世纪优秀人才支持计划。主持国家自然科学基金委重点项目、面上项目、教育部新世纪优秀人才基金、英国皇家学会“高级牛顿学者”基金和美国数学学会（AMS）访问基金等。主要从事随机微分方程以及相关领域的研究。近年来，在 SIAM 系列杂志、JDE、SPA 等期刊发表论文90余篇。出版一部专著《随机微分方程》和一部译著《随机微分方程：导论与应用》，当前为 IET Control Theory & Applications 编委。
报告摘要：The frequently used reduction technique is based on the chemical master equation for stochastic chemical kinetics with two-time scales, which yields the modified stochastic simulation algorithm (SSA). For the chemical reaction processes involving a large number of molecular species and reactions, the collection of slow reactions may still include a large number of molecular species and reactions. Consequently, the SSA is still computationally expensive. Because the chemical Langevin equations (CLEs) can effectively work for a large number of molecular species and reactions, this paper develops a reduction method based on the CLE by the stochastic averaging principle developed in the work of Khasminskii and Yin [SIAM J. Appl. Math. 56, 1766-1793 (1996); ibid. 56, 1794-1819 (1996)] to average out the fast-reacting variables. This reduction method leads to a limit averaging system, which is an approximation of the slow reactions. Because in the stochastic chemical kinetics, the CLE is seen as the approximation of the SSA, the limit averaging system can be treated as the approximation of the slow reactions. As an application, we examine the reduction of computation complexity for the gene regulatory networks with two-time scales driven by intrinsic noise. For linear and nonlinear protein production functions, the simulations show that the sample average (expectation) of the limit averaging system is close to that of the slow-reaction process based on the SSA. It demonstrates that the limit averaging system is an efficient approximation of the slow-reaction process in the sense of the weak convergence.
吴付科教授在做精彩报告
