時間：2019年12月16日（周一）09：00-10：00
地點：工業(yè)中心506室
報告題目：Modelling and anslysis of non-Markovian intracellular reaction networks
報告人簡介: 周天壽，男，教授，數(shù)學(xué)研究所所長，廣東省計算數(shù)學(xué)重點實驗室副主任，中國工業(yè)與應(yīng)用數(shù)學(xué)學(xué)會數(shù)學(xué)生命科學(xué)專業(yè)委員會副主任，中國運籌學(xué)會計算系統(tǒng)生物學(xué)專業(yè)委員會副主任。研究方向：計算系統(tǒng)生物學(xué)。在SCI刊物發(fā)表150余篇論文，包括國際頂尖刊物PNAS和PRL，其中發(fā)表在PRL上的論文被Harvard大學(xué)生物系M. Springer和J. Paulsson教授在Nature上作了專門評論。在科學(xué)出版社出版學(xué)術(shù)專著2部。曾獲全國優(yōu)秀博士學(xué)位論文獎和國家自然科學(xué)二等獎。主持4項國家自然科學(xué)基金委重點項目。
內(nèi)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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