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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
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:20260428T064123
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20201224T130000
DTEND;TZID=Asia/Seoul:20201224T140000
DTSTAMP:20260428T064123
CREATED:20210223T094304Z
LAST-MODIFIED:20210406T075331Z
UID:3983-1608814800-1608818400@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Ligand-receptor promiscuity enables cellular addressing
DESCRIPTION:We will discuss about “Ligand-receptor promiscuity enables cellular addressing”\, Su et al.\, bioRxiv (2021) \nIn multicellular organisms\, secreted ligands selectively activate\, or “address\,” specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors\, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue\, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands\, acting in combinations\, to address a larger number of individual cell types\, each defined by its receptor expression profile. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capacity. Combinatorial addressing extends to groups of cell types\, is robust to receptor expression noise\, grows more powerful with increasing receptor multiplicity\, and is maximized by specific biochemical parameter relationships. Together\, these results identify fundamental design principles governing cell addressing by ligand combinations.
URL:https://www.ibs.re.kr/bimag/event/2020-12-24_1/
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
END:VEVENT
END:VCALENDAR