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PRODID:-//Biomedical Mathematics Group - ECPv6.16.0//NONSGML v1.0//EN
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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20190101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20201224T020000
DTEND;TZID=Asia/Seoul:20201224T150000
DTSTAMP:20260513T065241
CREATED:20210223T094556Z
LAST-MODIFIED:20210406T075337Z
UID:3985-1608775200-1608822000@www.ibs.re.kr
SUMMARY:Dae Wook Kim\, Neural network aided approximation and parameter inference of stochastic models of gene expression
DESCRIPTION:We will discuss about “Neural network aided approximation and parameter inference of stochastic models of gene expression”\, Jian et al.\, bioRxiv (2020). \nNon-Markov 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-Markov models by the solutions of much simpler time-inhomogeneous Markov 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-Markov model. We show using a variety of models\, where the delays stem from transcriptional processes and feedback control\, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.
URL:https://www.ibs.re.kr/bimag/event/2020-12-24_2/
LOCATION:KAIST E2-1 room 3221\, E2-1 building\, Daejeon\, Daejeon\, 34141\, Korea\, Republic of
CATEGORIES:Journal Club,Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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