Neural network aided approximation and parameter inference of non-Markovian models
報告題目：Neural network aided approximation and parameter inference of non-Markovian models
時間：2024年11月2日 9:30-11:00
地點：主樓B座1421
邀請人：何仁初 教授
報告人簡介：曹志興，華東理工大學教授、博士生導師，中組部青年千人計劃入選者。2012年本科畢業于浙江大學控制科學與工程學系，2016年博士畢業于香港科技大學化學與生物分子工程學系，其先后于美國哈佛大學、英國愛丁堡大學擔任博士后。研究領域包括機器學習、系統生物學的交叉研究，多次以一作和通訊作者身份在Nature Communications、美國科學院院刊PNAS、Current Opinion in Biotechnology等著名期刊發表研究結果，成果入選《國家自然科學基金委員會2021年度報告》資助成果巡禮，獲得2021麻省理工科技評論亞太區35歲以下科技創新35人、2023阿里巴巴達摩院青橙獎最具潛力獎等榮譽。
報告摘要：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.