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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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X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230303T140000
DTEND;TZID=Asia/Seoul:20230303T160000
DTSTAMP:20260425T233158
CREATED:20230127T063333Z
LAST-MODIFIED:20230130T080633Z
UID:7280-1677852000-1677859200@www.ibs.re.kr
SUMMARY:Seho Park\, Dynamical information enables inference of gene regulation at single-cell scale
DESCRIPTION:We will discuss about “Dynamical information enables inference of gene regulation at single-cell scale”\, Zhang\, Stephen Y.\, and Michael PH Stumpf.\, bioRxiv (2023): 2023-01. \nAbstract \n\nCellular dynamics and emerging biological function are governed by patterns of gene expression arising from networks of interacting genes. Inferring these interactions from data is a notoriously difficult inverse problem that is central to systems biology. The majority of existing network inference methods work at the population level and construct a static representations of gene regulatory networks; they do not naturally allow for inference of differential regulation across a heterogeneous cell population. Building upon recent dynamical inference methods that model single cell dynamics using Markov processes\, we propose locaTE\, an information-theoretic approach which employs a localised transfer entropy to infer cell-specific\, causal gene regulatory networks. LocaTE uses high-resolution estimates of dynamics and geometry of the cellular gene expression manifold to inform inference of regulatory interactions. We find that this approach is generally superior to using static inference methods\, often by a significant margin. We demonstrate that factor analysis can give detailed insights into the inferred cell-specific GRNs. In application to two experimental datasets\, we recover key transcription factors and regulatory interactions that drive mouse primitive endoderm formation and pancreatic development. For both simulated and experimental data\, locaTE provides a powerful\, efficient and scalable network inference method that allows us to distil cell-specific networks from single cell data.
URL:https://www.ibs.re.kr/bimag/event/2023-03-03-jc/
LOCATION:B378 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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230310T140000
DTEND;TZID=Asia/Seoul:20230310T160000
DTSTAMP:20260425T233158
CREATED:20230228T075546Z
LAST-MODIFIED:20230309T012315Z
UID:7391-1678456800-1678464000@www.ibs.re.kr
SUMMARY:Eui Min Jung\, Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks
DESCRIPTION:We will discuss about “Antithetic Integral Feedback Ensures Robust Perfect Adaptation in Noisy Biomolecular Networks”\, Briat\, Corentin\, Ankit Gupta\, and Mustafa Khammash. Cell systems 2.1 (2016): 15-26. \nAbstract \n\nThe ability to adapt to stimuli is a defining feature of many biological systems and critical to maintaining homeostasis. While it is well appreciated that negative feedback can be used to achieve homeostasis when networks behave deterministically\, the effect of noise on their regulatory function is not understood. Here\, we combine probability and control theory to develop a theory of biological regulation that explicitly takes into account the noisy nature of biochemical reactions. We introduce tools for the analysis and design of robust homeostatic circuits and propose a new regulation motif\, which we call antithetic integral feedback. This motif exploits stochastic noise\, allowing it to achieve precise regulation in scenarios where similar deterministic regulation fails. Specifically\, antithetic integral feedback preserves the stability of the overall network\, steers the population of any regulated species to a desired set point\, and adapts perfectly. We suggest that this motif may be prevalent in endogenous biological circuits and useful when creating synthetic circuits.
URL:https://www.ibs.re.kr/bimag/event/2023-03-10-jc/
LOCATION:B378 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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230317T140000
DTEND;TZID=Asia/Seoul:20230317T160000
DTSTAMP:20260425T233158
CREATED:20230228T075515Z
LAST-MODIFIED:20230315T020610Z
UID:7393-1679061600-1679068800@www.ibs.re.kr
SUMMARY:Olive Cawiding\, Single-sample landscape entropy reveals the imminent phase transition during disease progression
DESCRIPTION:We will discuss about “Single-sample landscape entropy reveals the imminent phase transition during disease progression”\, Liu R\, Chen P\, Chen L.\, Bioinformatics. 2020 Mar 1;36(5):1522-1532. \nAbstract \n\n\nMotivation: The time evolution or dynamic change of many biological systems during disease progression is not always smooth but occasionally abrupt\, that is\, there is a tipping point during such a process at which the system state shifts from the normal state to a disease state. It is challenging to predict such disease state with the measured omics data\, in particular when only a single sample is available. \nResults: In this study\, we developed a novel approach\, i.e. single-sample landscape entropy (SLE) method\, to identify the tipping point during disease progression with only one sample data. Specifically\, by evaluating the disorder of a network projected from a single-sample data\, SLE effectively characterizes the criticality of this single sample network in terms of network entropy\, thereby capturing not only the signals of the impending transition but also its leading network\, i.e. dynamic network biomarkers. Using this method\, we can characterize sample-specific state during disease progression and thus achieve the disease prediction of each individual by only one sample. Our method was validated by successfully identifying the tipping points just before the serious disease symptoms from four real datasets of individuals or subjects\, including influenza virus infection\, lung cancer metastasis\, prostate cancer and acute lung injury.
URL:https://www.ibs.re.kr/bimag/event/2023-03-17-jc/
LOCATION:B378 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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230324T140000
DTEND;TZID=Asia/Seoul:20230324T160000
DTSTAMP:20260425T233158
CREATED:20230228T075941Z
LAST-MODIFIED:20230228T075941Z
UID:7395-1679666400-1679673600@www.ibs.re.kr
SUMMARY:Candan Celik\, The effect of microRNA on protein variability and gene expression fidelity
DESCRIPTION:We will discuss about “The effect of microRNA on protein variability and gene expression fidelity”\, Hilfinger\, Andreas\, and Raymond Fan.\, Biophysical journal 122.3 (2023): 537a. \nAbstract \n\nSmall regulatory RNA molecules such as microRNA modulate gene expression through inhibiting the translation of messenger RNA (mRNA). Such post-transcriptional regulation has been recently hypothesized to reduce the stochastic variability of gene expression around average levels. Here we quantify noise in stochastic gene expression models with and without such regulation. Our results suggest that silencing mRNA post-transcriptionally will always increase rather than decrease gene expression noise when the silencing of mRNA also increases its degradation as is expected for microRNA interactions with mRNA. In that regime we also find that silencing mRNA generally reduces the fidelity of signal transmission from deterministically varying upstream factors to protein levels. These findings suggest that microRNA binding to mRNA does not generically confer precision to protein expression
URL:https://www.ibs.re.kr/bimag/event/2023-03-24-jc/
LOCATION:B378 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
END:VEVENT
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