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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210902T100000
DTEND;TZID=Asia/Seoul:20210902T110000
DTSTAMP:20260510T081345
CREATED:20210901T160000Z
LAST-MODIFIED:20211230T030825Z
UID:4540-1630576800-1630580400@www.ibs.re.kr
SUMMARY:Exploiting evolution to design better cancer therapies
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)\n\nAbstract: Our current approach to cancer treatment has been largely driven by finding molecular targets\, those patients fortunate enough to have a targetable mutation will receive a fixed treatment schedule designed to deliver the maximum tolerated dose (MTD). These therapies generally achieve impressive short-term responses\, that unfortunately give way to treatment resistance and tumor relapse. The importance of evolution during both tumor progression\, metastasis and treatment response is becoming more widely accepted. However\, MTD treatment strategies continue to dominate the precision oncology landscape and ignore the fact that treatments drive the evolution of resistance. Here we present an integrated theoretical/experimental/clinical approach to develop treatment strategies that specifically embrace cancer evolution. We will consider the importance of using treatment response as a critical driver of subsequent treatment decisions\, rather than fixed strategies that ignore it. We will also consider using mathematical models to drive treatment decisions based on limited clinical data. Through the integrated application of mathematical and experimental models as well as clinical data we will illustrate that\, evolutionary therapy can drive either tumor control or extinction using a combination of drug treatments and drug holidays. Our results strongly indicate that the future of precision medicine shouldn’t be in the development of new drugs but rather in the smarter evolutionary\, and model informed\, application of preexisting ones.
URL:https://www.ibs.re.kr/bimag/event/2021-09-02/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/09/AndersonAlexander2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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DTSTART;TZID=Asia/Seoul:20210902T130000
DTEND;TZID=Asia/Seoul:20210902T140000
DTSTAMP:20260510T081345
CREATED:20210902T190000Z
LAST-MODIFIED:20210831T052727Z
UID:4841-1630587600-1630591200@www.ibs.re.kr
SUMMARY:Machine learning of stochastic gene network phenotypes
DESCRIPTION:We will discuss about “Machine learning of stochastic gene network phenotypes”\, Park et al.\, bioRxiv\, 2019 \nAbstract: \nA recurrent challenge in biology is the development of predictive quantitative models because most molecular and cellular parameters have unknown values and realistic models are analytically intractable. While the dynamics of the system can be analyzed via computer simulations\, substantial computational resources are often required given uncertain parameter values resulting in large numbers of parameter combinations\, especially when realistic biological features are included. Simulation alone also often does not yield the kinds of intuitive insights from analytical solutions. Here we introduce a general framework combining stochastic/mechanistic simulation of reaction systems and machine learning of the simulation data to generate computationally efficient predictive models and interpretable parameter-phenotype maps. We applied our approach to investigate stochastic gene expression propagation in biological networks\, which is a contemporary challenge in the quantitative modeling of single-cell heterogeneity. We found that accurate\, predictive machine-learning models of stochastic simulation results can be constructed. Even in the simplest networks existing analytical schemes generated significantly less accurate predictions than our approach\, which revealed interesting insights when applied to more complex circuits\, including the extensive tunability of information propagation enabled by feedforward circuits and how even single negative feedbacks can utilize stochastic fluctuations to generate robust oscillations. Our approach is applicable beyond biology and opens up a new avenue for exploring complex dynamical systems.
URL:https://www.ibs.re.kr/bimag/event/2021-09-02-2/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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