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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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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230804T140000
DTEND;TZID=Asia/Seoul:20230804T160000
DTSTAMP:20260425T124045
CREATED:20230729T064450Z
LAST-MODIFIED:20230730T231258Z
UID:8131-1691157600-1691164800@www.ibs.re.kr
SUMMARY:Seokhwan Moon\, The Internal Model Principle for Biomolecular Control Theory
DESCRIPTION:We will discuss about “ The Internal Model Principle for Biomolecular Control Theory ”\, Gupta\, Ankit\, and Mustafa Khammash.\, IEEE Open Journal of Control Systems 2 (2023): 63-69. \n  \nAbstract \nThe well-known Internal Model Principle (IMP) is a cornerstone of modern control theory. It stipulates the necessary conditions for asymptotic robustness of disturbance-prone dynamical systems by asserting that such a system must embed a subsystem in a feedback loop\, and this subsystem must be able to reduplicate the dynamic disturbance using only the regulated variable as the input. The insights provided by IMP can help in both designing suitable controllers and also in analysing the regulatory mechanisms in complex systems. So far the application of IMP in biology has been case-specific and ad hoc\, primarily due to the lack of generic versions of the IMP for biomolecular reaction networks that model biological processes. In this short article we highlight the need for an IMP in biology and discuss a recently developed version of it for biomolecular networks that exhibit maximal Robust Perfect Adaptation (maxRPA) by being robust to the maximum number of disturbance sources.
URL:https://www.ibs.re.kr/bimag/event/2023-08-04-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230811T150000
DTEND;TZID=Asia/Seoul:20230811T170000
DTSTAMP:20260425T124045
CREATED:20230730T230909Z
LAST-MODIFIED:20230809T124859Z
UID:8141-1691766000-1691773200@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Decomposing predictability to identify dominant causal drivers in complex ecosystems
DESCRIPTION:We will discuss about “ Decomposing predictability to identify dominant causal drivers in complex ecosystems ”\,Suzuki\, Kenta\, Shin-ichiro S. Matsuzaki\, and Hiroshi Masuya.\, Proceedings of the National Academy of Sciences 119.42 (2022): e2204405119. \n  \nAbstract \n\nEcosystems are complex systems of various physical\, biological\, and chemical processes. Since ecosystem dynamics are composed of a mixture of different levels of stochasticity and nonlinearity\, handling these data is a challenge for existing methods of time series–based causal inferences. Here\, we show that\, by harnessing contemporary machine learning approaches\, the concept of Granger causality can be effectively extended to the analysis of complex ecosystem time series and bridge the gap between dynamical and statistical approaches. The central idea is to use an ensemble of fast and highly predictive artificial neural networks to select a minimal set of variables that maximizes the prediction of a given variable. It enables decomposition of the relationship among variables through quantifying the contribution of an individual variable to the overall predictive performance. We show how our approach\, EcohNet\, can improve interaction network inference for a mesocosm experiment and simulated ecosystems. The application of the method to a long-term lake monitoring dataset yielded interpretable results on the drivers causing cyanobacteria blooms\, which is a serious threat to ecological integrity and ecosystem services. Since performance of EcohNet is enhanced by its predictive capabilities\, it also provides an optimized forecasting of overall components in ecosystems. EcohNet could be used to analyze complex and hybrid multivariate time series in many scientific areas not limited to ecosystems.
URL:https://www.ibs.re.kr/bimag/event/2023-08-11-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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