报告题目：Neural network aided approximation and parameter inference of non-Markovian models

时间：2024年11月2日  9:30-11:00

地点：主楼B座1421

邀请人：何仁初 教授

报告人简介：曹志兴，华东理工大学教授、博士生导师，中组部青年千人计划入选者。2012年本科毕业于浙江大学控制科学与工程学系，2016年博士毕业于香港科技大学化学与生物分子工程学系，其先后于美国哈佛大学、英国爱丁堡大学担任博士后。研究领域包括机器学习、系统生物学的交叉研究，多次以一作和通讯作者身份在Nature Communications、美国科学院院刊PNAS、Current Opinion in Biotechnology等著名期刊发表研究结果，成果入选《国家自然科学基金委员会2021年度报告》资助成果巡礼，获得2021麻省理工科技评论亚太区35岁以下科技创新35人、2023阿里巴巴达摩院青橙奖最具潜力奖等荣誉。

报告摘要：Non-Markovian models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models, as well as the inference of their parameters from data, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non- Markovian models by the solutions of much simpler time-inhomogeneous Markovian models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markovian model. We show using a variety of models, where the delays stem from transcriptional processes and feedback control, that the Markovian models learnt by the neural network accurately reflect the stochastic dynamics across parameter space. Finally, I will talk about how to publish a high-profile paper given the example presented above.