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CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
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:20200101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211118T110000
DTEND;TZID=Asia/Seoul:20211118T120000
DTSTAMP:20260424T232019
CREATED:20211117T170000Z
LAST-MODIFIED:20211230T031249Z
UID:4820-1637233200-1637236800@www.ibs.re.kr
SUMMARY:Following the energy in cellular information processing
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: John Hopfield first pointed out that there are barriers – we call them Hopfield barriers – to biological information-processing at thermodynamic equilibrium. I will explain how the widely-used Hill function with coefficient n is the universal Hopfield barrier to the sharpness of binding to n sites. Away from thermodynamic equilibrium\, I will describe the challenge of path dependent  complexity and introduce the entropy-production index as a measure of non-equilibrium complexity.
URL:https://www.ibs.re.kr/bimag/event/2021-11-18/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/11/jeremy-scaled_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211112T133000
DTEND;TZID=Asia/Seoul:20211112T153000
DTSTAMP:20260424T232019
CREATED:20211111T210000Z
LAST-MODIFIED:20211109T062309Z
UID:5120-1636723800-1636731000@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its application 2/4
DESCRIPTION:Simplicial Complexes\, Persistent Homology and Persistent Diagrams. After a brief review on the persistent homology( Cohen-Steiner\, Edelsbrunner\, Harer\,2010)\, we discuss constructive procedures persistent diagrams from the persistent homology. Code; 9 software packages generating persistent homology are introduced at ” A roadmap for the computation of persistent homology”\, EPJ Data Science\, a Springer Open Journal.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211112T110000
DTEND;TZID=Asia/Seoul:20211112T120000
DTSTAMP:20260424T232019
CREATED:20211111T170000Z
LAST-MODIFIED:20211111T084242Z
UID:5185-1636714800-1636718400@www.ibs.re.kr
SUMMARY:Detecting and quantifying causal associations in large nonlinear time series datasets
DESCRIPTION:We will discuss about “Detecting and quantifying causal associations in large nonlinear time series datasets”\, Runge et al.\, Science Advances\, 2019 \nIdentifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here\, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power\, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields.
URL:https://www.ibs.re.kr/bimag/event/2021-11-12-2/
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:20211111T110000
DTEND;TZID=Asia/Seoul:20211111T120000
DTSTAMP:20260424T232019
CREATED:20211110T170000Z
LAST-MODIFIED:20210826T000813Z
UID:4816-1636628400-1636632000@www.ibs.re.kr
SUMMARY:Biofluiddynamics of reproduction
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: From fertilization to birth\, successful mammalian reproduction relies on interactions of elastic structures with a fluid environment. Sperm flagella must move through cervical mucus to the uterus and into the oviduct\, where fertilization occurs. In fact\, some sperm may adhere to oviductal epithelia\, and must change their pattern of oscillation to escape. In addition\, coordinated beating of oviductal cilia also drive the flow. Sperm-egg penetration\, transport of the fertilized ovum from the oviduct to its implantation in the uterus and\, indeed\, birth itself are rich examples of elasto-hydrodynamic coupling. We will discuss successes and challenges in the mathematical and computational modeling of the biofluids of reproduction.
URL:https://www.ibs.re.kr/bimag/event/2021-11-11/
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/2021/07/fauci_profile_sqr-e1627697850446.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211029T150000
DTEND;TZID=Asia/Seoul:20211029T170000
DTSTAMP:20260424T232019
CREATED:20211028T210000Z
LAST-MODIFIED:20211029T113540Z
UID:5117-1635519600-1635526800@www.ibs.re.kr
SUMMARY:The Graph convolutional Networks (GCN) with Persistent Homology and its application 1/4
DESCRIPTION:(1) GCN and its Application.\nWe introduce the GCN by reviewing the monumental paper ” Semi-Supervised Classification with the Graph Convolutional Networks”\, ICLR 2018 by Kipf and Welling. We are going to much detail the algorithm of message aggregation and passings and learning processes.\nCode ; https://github.com/tkipf/gcn \n(2) Graph Attention networks(GAT) and its Applications. Bengio et al improved greatly the capability of GCN by employing the Attention mechanism to GCN on the paper\,” Graph Attention Networks\, ICLR\,2018. We review closely the derivation of algirithms\, learning processes and discuss its super performance.\nCode; https://github.com/PetarV-/GAT
URL:https://www.ibs.re.kr/bimag/event/2021-10-29/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211027T170000
DTEND;TZID=Asia/Seoul:20211027T180000
DTSTAMP:20260424T232019
CREATED:20211026T230000Z
LAST-MODIFIED:20210901T070035Z
UID:4812-1635354000-1635357600@www.ibs.re.kr
SUMMARY:Systems pharmacology towards personalized chronotherapy
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: \nChronotherapeutics- that is administering drugs following the patient’s biological rhythms over the 24 h span- may largely impact on both drug toxicities and efficacy in various pathologies including cancer [1]. However\, recent findings highlight the critical need of personalizing circadian delivery according to the patient sex\, genetic background or chronotype. Chronotherapy personalization requires to reliably account for the temporal dynamics of molecular pathways of patient’s response to drug administration [2]. In a context where clinical molecular data is usually minimal in individual patients\, multi-scale- from preclinical to clinical- systems pharmacology stands as an adapted solution to describe gene and protein networks driving circadian rhythms of treatment efficacy and side effects and allow for the design of personalized chronotherapies.\nSuch a multiscale approach is being undertaken for personalizing the circadian administration of irinotecan\, one of the cornerstones of chemotherapies against digestive cancers. Irinotecan molecular chronopharmacology was studied at the cellular level in an in vitro/in silico investigation. Large transcription rhythms of period T= 28 h 06 min (SD 1 h 41 min) moderated drug bioactivation\, detoxification\, transport\, and target in synchronized Caco-2 colorectal cancer cell cultures. These molecular rhythms translated into statistically significant changes according to drug timing in irinotecan pharmacokinetics\, pharmacodynamics\, and drug-induced apoptosis. Clock silencing through siBMAL1 exposure ablated all the chronopharmacology mechanisms. Mathematical modeling highlighted circadian bioactivation and detoxification as the most critical determinants of irinotecan chronopharmacology [3]. The cellular model of irinotecan chronoPK-PD was further tested on SW480 and SW620 cell lines\, and connected to a new clock model to investigate the feasibility of irinotecan timing personalization solely based on clock gene expression monitoring (Hesse\, Martinelli et al.\, under review).\nTo step towards the clinics\, on one side\, mathematical models of irinotecan\, oxaliplatin and 5-fluorouracil pharmacokinetics were designed to precisely compute the exposure concentration of tissue over time after complex chronomodulated drug administration through programmable pumps [4]. On the other side\, we aimed to design a model learning methodology predicting from non-invasively measured circadian biomarkers (e.g. rest-activity\, body temperature\, cortisol\, food intake\, melatonin)\, the patient peripheral circadian clocks and associated optimal drug timing [5]. We investigated at the molecular scale the influence of systemic regulators on peripheral clocks in four classes of mice (2 strains\, 2 sexes). Best models involved a modulation of either Bmal1 or Per2 transcription most likely by temperature or nutrient exposure cycles. The strengths of systemic regulations were found to be significantly different according to mouse sex and genetic background.\nReferences\n1. Ballesta\, A.\, et al.\, Systems Chronotherapeutics. Pharmacol Rev\, 2017. 69(2): p. 161-199.\n2. Sancar\, A. and R.N. Van Gelder\, Clocks\, cancer\, and chronochemotherapy. Science\, 2021. 371(6524).\n3. Dulong\, S.\, et al.\, Identification of Circadian Determinants of Cancer Chronotherapy through In Vitro Chronopharmacology and Mathematical Modeling. Mol Cancer Ther\, 2015.\n4. Hill\, R.J.W.\, et al.\, Optimizing circadian drug infusion schedules towards personalized cancer chronotherapy. PLoS Comput Biol\, 2020. 16(1): p. e1007218.\n5. Martinelli\, J.\, et al.\, Model learning to identify systemic regulators of the peripheral circadian clock. 2021. \n 
URL:https://www.ibs.re.kr/bimag/event/2021-10-27/
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/2021/07/AnnabelleBallesta_profile_sqr-e1627697556509.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211027T110000
DTEND;TZID=Asia/Seoul:20211027T120000
DTSTAMP:20260424T232019
CREATED:20211028T190000Z
LAST-MODIFIED:20211024T160104Z
UID:5058-1635332400-1635336000@www.ibs.re.kr
SUMMARY:Inferring causality in biological oscillators
DESCRIPTION:Abstract \nTBA
URL:https://www.ibs.re.kr/bimag/event/2021-10-29-2/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211022T130000
DTEND;TZID=Asia/Seoul:20211022T140000
DTSTAMP:20260424T232019
CREATED:20211021T190000Z
LAST-MODIFIED:20211001T062513Z
UID:5061-1634907600-1634911200@www.ibs.re.kr
SUMMARY:Filtering and inference for stochastic oscillators with distributed delays
DESCRIPTION:We will discuss about “Filtering and inference for stochastic oscillators with distributed delays”\, Calderazzo et al.\, Bioinformatics\, 2018 at the Journal Club \n\n\n\n\nMotivation\nThe time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data\, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model\, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here. \n\n\nResults\nWe develop a novel filtering approach for the LNA in stochastic systems with distributed delays\, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1\, a key gene involved in the mammalian central circadian clock\, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus. \n\n\nAvailability and implementation\nProgrammes are written in MATLAB and Statistics Toolbox Release 2016 b\, The MathWorks\, Inc.\, Natick\, Massachusetts\, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.
URL:https://www.ibs.re.kr/bimag/event/2021-10-22-2/
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:20211021T110000
DTEND;TZID=Asia/Seoul:20211021T120000
DTSTAMP:20260424T232019
CREATED:20211103T170000Z
LAST-MODIFIED:20210930T040222Z
UID:4787-1634814000-1634817600@www.ibs.re.kr
SUMMARY:Scaling in development
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)\n\nAbstract: \n Within a given species\, fluctuations in egg or embryo size is unavoidable. Despite this\, the gene expression pattern and hence the embryonic structure often scale in proportion with the body length. This scaling phenomenon is very common in development and regeneration and has long fascinated scientists. I will first discuss a generic theoretical framework to show how scaling gene expression pattern can emerge from non-scaling morphogen gradients. I will then demonstrate that the Drosophila gap gene system achieves scaling in a way that is entirely consistent with our theory. Remarkably\, a parameter-free model based on the theory quantitatively accounts for the gap gene expression pattern in nearly all morphogen mutants. Furthermore\, the regulation logic and the coding/decoding strategy of the gap gene system can be revealed. Our work provides a general theoretical framework on a large class of problems where scaling output is induced by non-scaling input\, as well as a unified understanding of scaling\, mutants’ behavior and regulation in the Drosophila gap gene and related systems.
URL:https://www.ibs.re.kr/bimag/event/2021-10-21/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/07/resize.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211008T140000
DTEND;TZID=Asia/Seoul:20211008T150000
DTSTAMP:20260424T232019
CREATED:20211007T190000Z
LAST-MODIFIED:20211006T081805Z
UID:4912-1633701600-1633705200@www.ibs.re.kr
SUMMARY:Balanced truncation for model reduction of biological oscillators
DESCRIPTION:We will discuss about “Balanced truncation for model reduction of biological oscillators”\, Padoan et al.\, Biological Cybernetics\, 2021 \nModel reduction is a central problem in mathematical biology. Reduced order models enable modeling of a biological system at different levels of complexity and the quantitative analysis of its properties\, like sensitivity to parameter variations and resilience to exogenous perturbations. However\, available model reduction methods often fail to capture a diverse range of nonlinear behaviors observed in biology\, such as multistability and limit cycle oscillations. The paper addresses this need using differential analysis. This approach leads to a nonlinear enhancement of classical balanced truncation for biological systems whose behavior is not restricted to the stability of a single equilibrium. Numerical results suggest that the proposed framework may be relevant to the approximation of classical models of biological systems.
URL:https://www.ibs.re.kr/bimag/event/2021-10-8/
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:20211007T110000
DTEND;TZID=Asia/Seoul:20211007T120000
DTSTAMP:20260424T232019
CREATED:20211006T170000Z
LAST-MODIFIED:20211230T031435Z
UID:4850-1633604400-1633608000@www.ibs.re.kr
SUMMARY:A temporal signaling code to specify immune responses
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: \nImmune sentinel cells must initiate the appropriate immune response upon sensing the presence of diverse pathogens or immune stimuli. To generate stimulus-specific gene expression responses\, immune sentinel cells have evolved a temporal code in the dynamics of stimulus responsive transcription factors. I will present recent works 1) using an information theoretic approach to identify the codewords\, termed “signaling codons”\, 2) using a machine learning approach to characterize their reliability and points of confusion\, and 3) dynamical systems modeling to characterize the molecular circuits that allow for their encoding. I will present progress on how the temporal code may be decoded to specify immune responses.  Further\, I will discuss to what extent such a code may be harnessed to achieve greater pharmacological specificity when therapeutically targeting pleiotropic signaling hubs. \nNFκB Signaling: information theory\, signaling codons \nAdelaja\, A.\, Taylor\, B.\, Sheu\, K.M.\, Liu\, Y.\, Luecke\, S.\, Hoffmann\, A. 2021 Six distinct NFκB signaling codons convey discrete information to distinguish stimuli and enable appropriate macrophage responses. Immunity\, 54\, pp.916-930. e7. PMID: 33979588 \nTang\, Y.\, Adelaja\, A.\, Ye\, X\, Deeds\, E.\, Wollman\, R.\, Hoffmann\, A. 2021. Quantifying information accumulation encoded in the dynamics of biochemical signaling. Nature Communications 12\, pp.1-10 \nDecoding signaling codons to specify immune responses \nSen S.\, Cheng\, Z.\, Sheu\, K.\, Chen\, E.Y.H.\, Hoffmann\, A. 2020 Gene Regulatory Strategies that Decode the Duration of NFkB Dynamics Contribute to LPS- versus TNF-Specific Gene Expression. Cell Systems\, 10\, pp.1-14. PMID:31972132\, PMC7047529 \nCheng\, Q.J.\, Ohta\, S.\, Sheu\, K.M.\, Spreafico\, R.\, Adelaja\, A.\, Taylor\, B.\, Hoffmann\, A.  2021 NFκB dynamics determine the stimulus-specificity of epigenomic reprogramming in macrophages. Science\, 372\, pp.1349-1353; PMID: 34140389. \nPharmacologic manipulation of the code \nBehar\, M.\, Barken\, D.\, Werner\, S.L.\, Hoffmann\, A. 2013  The Dynamics of Signaling as a Pharmacological Target.  Cell\, 155\, pp.448-461. PMID: 24120141\, PMC3856316
URL:https://www.ibs.re.kr/bimag/event/2021-10-07/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/10/AlexanderHoffmann_profile_250x250.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210924T130000
DTEND;TZID=Asia/Seoul:20210924T140000
DTSTAMP:20260424T232019
CREATED:20210922T190000Z
LAST-MODIFIED:20210831T052818Z
UID:4910-1632488400-1632492000@www.ibs.re.kr
SUMMARY:A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells
DESCRIPTION:We will discuss about “A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells”\, Unosson et al.\, bioRxiv\, 2021 \nWe propose a stochastic distributed delay model together with a Markov random field prior and a measurement model for bioluminescence-reporting to analyse spatiotemporal gene expression in intact networks of cells. The model describes the oscillating time evolution of molecular mRNA counts through a negative transcriptional-translational feedback loop encoded in a chemical Langevin equation with a probabilistic delay distribution. The model is extended spatially by means of a multiplicative random effects model with a first order Markov random field prior distribution. Our methodology effectively separates intrinsic molecular noise\, measurement noise\, and extrinsic noise and phenotypic variation driving cell heterogeneity\, while being amenable to parameter identification and inference. Based on the single-cell model we propose a novel computational stability analysis that allows us to infer two key characteristics\, namely the robustness of the oscillations\, i.e. whether the reaction network exhibits sustained or damped oscillations\, and the profile of the regulation\, i.e. whether the inhibition occurs over time in a more distributed versus a more direct manner\, which affects the cells’ ability to phase-shift to new schedules. We show how insight into the spatio-temporal characteristics of the circadian feedback loop in the suprachiasmatic nucleus (SCN) can be gained by applying the methodology to bioluminescence-reported expression of the circadian core clock gene Cry1 across mouse SCN tissue. We find that while (almost) all SCN neurons exhibit robust cell-autonomous oscillations\, the parameters that are associated with the regulatory transcription profile give rise to a spatial division of the tissue between the central region whose oscillations are resilient to perturbation in the sense that they maintain a high degree of synchronicity\, and the dorsal region which appears to phase shift in a more diversified way as a response to large perturbations and thus could be more amenable to entrainment.
URL:https://www.ibs.re.kr/bimag/event/2021-09-24/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210917T130000
DTEND;TZID=Asia/Seoul:20210917T140000
DTSTAMP:20260424T232019
CREATED:20210915T190000Z
LAST-MODIFIED:20210831T052758Z
UID:4908-1631883600-1631887200@www.ibs.re.kr
SUMMARY:The Oscillation Amplitude\, Not the Frequency of Cytosolic Calcium\, Regulates Apoptosis Induction
DESCRIPTION:We will discuss about “The Oscillation Amplitude\, Not the Frequency of Cytosolic Calcium\, Regulates Apoptosis Induction ”\, Qi et al.\, iScience\, 2020 \nAbstract: \nAlthough a rising concentration of cytosolic Ca2+ has long been recognized as an essential signal for apoptosis\, the dynamical mechanisms by which Ca2+ regulates apoptosis are not clear yet. To address this\, we constructed a computational model that integrates known biochemical reactions and can reproduce the dynamical behaviors of Ca2+-induced apoptosis as observed in experiments. Model analysis shows that oscillating Ca2+ signals first convert into gradual signals and eventually transform into a switch-like apoptotic response. Via the two processes\, the apoptotic signaling pathway filters the frequency of Ca2+ oscillations effectively but instead responds acutely to their amplitude. Collectively\, our results suggest that Ca2+ regulates apoptosis mainly via oscillation amplitude\, rather than frequency\, modulation. This study not only provides a comprehensive understanding of how oscillatory Ca2+ dynamically regulates the complex apoptotic signaling network but also presents a typical example of how Ca2+ controls cellular responses through amplitude modulation.
URL:https://www.ibs.re.kr/bimag/event/2021-09-17/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210916T110000
DTEND;TZID=Asia/Seoul:20210916T120000
DTSTAMP:20260424T232019
CREATED:20210915T170000Z
LAST-MODIFIED:20211230T030915Z
UID:4529-1631790000-1631793600@www.ibs.re.kr
SUMMARY:Stochastic processes as scientific instruments: efficient inference based on stochastic dynamical systems
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)\n\nAbstract: Questions about the mechanistic operation of biological systems are naturally formulated as stochastic processes\, but confronting such models with data can be challenging.  In this talk\, I describe the essence of the difficulty\, highlighting both the technical issues and the importance of the “plug-and-play property”.  I then illustrate some effective approaches to efficient inference based on such models.  I conclude by sketching promising new developments and describing some open problems.
URL:https://www.ibs.re.kr/bimag/event/2021-09-16/
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/imagev2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210909T110000
DTEND;TZID=Asia/Seoul:20210909T120000
DTSTAMP:20260424T232019
CREATED:20210902T140000Z
LAST-MODIFIED:20210903T055016Z
UID:4981-1631185200-1631188800@www.ibs.re.kr
SUMMARY:COVID19 – Mathematical Modeling and Machine Learning
DESCRIPTION:Abstract \nThis presentation include the following two topics. First of all\, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore\, we validated the model with the conventional model to confirm its convergent nature. Next\, We also developed a machine learning model that predicts the mortality of infected patients by using basic patients information such as age\, residence\, comorbidity\, and past medical history. Furthermore\, we aim to establish a medical system that allows patients to check their own severity\, and informs them to visit the appropriate clinic center by referring to the past treatment details of other patients with similar severity.
URL:https://www.ibs.re.kr/bimag/event/covid19-mathematical-modeling-and-machine-learning/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210909T090000
DTEND;TZID=Asia/Seoul:20210909T100000
DTSTAMP:20260424T232019
CREATED:20210908T190000Z
LAST-MODIFIED:20210903T055048Z
UID:4906-1631178000-1631181600@www.ibs.re.kr
SUMMARY:Nonlinear delay differential equations and their application to modeling biological network motifs
DESCRIPTION:We will discuss about “Nonlinear delay differential equations and their application to modeling biological network motifs”\, Glass et al.\, Nature Communications\, 2021 \nAbstract: \nBiological regulatory systems\, such as cell signaling networks\, nervous systems and ecological webs\, consist of complex dynamical interactions among many components. Network motif models focus on small sub-networks to provide quantitative insight into overall behavior. However\, such models often overlook time delays either inherent to biological processes or associated with multi-step interactions. Here we systematically examine explicit-delay versions of the most common network motifs via delay differential equation (DDE) models\, both analytically and numerically. We find many broadly applicable results\, including parameter reduction versus canonical ordinary differential equation (ODE) models\, analytical relations for converting between ODE and DDE models\, criteria for when delays may be ignored\, a complete phase space for autoregulation\, universal behaviors of feedforward loops\, a unified Hill-function logic framework\, and conditions for oscillations and chaos. We conclude that explicit-delay modeling simplifies the phenomenology of many biological networks and may aid in discovering new functional motifs.
URL:https://www.ibs.re.kr/bimag/event/2021-09-09/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210908T170000
DTEND;TZID=Asia/Seoul:20210908T180000
DTSTAMP:20260424T232019
CREATED:20210907T230000Z
LAST-MODIFIED:20210907T103108Z
UID:4648-1631120400-1631124000@www.ibs.re.kr
SUMMARY:[CANCELED] Approaches to understanding tumour-immune interactions
DESCRIPTION:CANCELED due to unexpected circumstances\nThis talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: While the presence of immune cells within solid tumours was initially viewed positively\, as the host fighting to rid itself of a foreign body\, we now know that the tumour can manipulate immune cells so that they promote\, rather than inhibit\, tumour growth. Immunotherapy aims to correct for this by boosting and/or restoring the normal function of the immune system. Immunotherapy has delivered some extremely promising results. However\, the complexity of the tumour-immune interactions means that it can be difficult to understand why one patient responds well to immunotherapy while another does not. In this talk\, we will show how mathematical\, statistical and topological methods can contribute to resolving this issue and present recent results which illustrate the complementary insight that different approaches can deliver.
URL:https://www.ibs.re.kr/bimag/event/2021-09-08/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/png:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/06/Helen-Byrne_Photo_crop2.png
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210902T130000
DTEND;TZID=Asia/Seoul:20210902T140000
DTSTAMP:20260424T232019
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210902T100000
DTEND;TZID=Asia/Seoul:20210902T110000
DTSTAMP:20260424T232019
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210819T130000
DTEND;TZID=Asia/Seoul:20210819T140000
DTSTAMP:20260424T232019
CREATED:20210819T190000Z
LAST-MODIFIED:20210812T095509Z
UID:4839-1629378000-1629381600@www.ibs.re.kr
SUMMARY:Cellular signaling beyond the Wiener-Kolmogorov limit
DESCRIPTION:We will discuss about “Cellular signaling beyond the Wiener-Kolmogorov limit”\, Weisenberger et al.\, bioRxiv\, 2021 \nAbstract: \nAccurate propagation of signals through stochastic biochemical networks involves significant expenditure of cellular resources. The same is true for regulatory mechanisms that suppress fluctuations in biomolecular populations. Wiener-Kolmogorov (WK) optimal noise filter theory\, originally developed for engineering problems\, has recently emerged as a valuable tool to estimate the maximum performance achievable in such biological systems for a given metabolic cost. However\, WK theory has one assumption that potentially limits its applicability: it relies on a linear\, continuum description of the reaction dynamics. Despite this\, up to now no explicit test of the theory in nonlinear signaling systems with discrete molecular populations has ever seen performance beyond the WK bound. Here we report the first direct evidence the bound being broken. To accomplish this\, we develop a theoretical framework for multi-level signaling cascades\, including the possibility of feedback interactions between input and output. In the absence of feedback\, we introduce an analytical approach that allows us to calculate exact moments of the stationary distribution for a nonlinear system. With feedback\, we rely on numerical solutions of the system’s master equation. The results show WK violations in two common network motifs: a two-level signaling cascade and a negative feedback loop. However the magnitude of the violation is biologically negligible\, particularly in the parameter regime where signaling is most effective. The results demonstrate that while WK theory does not provide strict bounds\, its predictions for performance limits are excellent approximations\, even for nonlinear systems. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2021-08-19/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210813T110000
DTEND;TZID=Asia/Seoul:20210813T120000
DTSTAMP:20260424T232019
CREATED:20210727T190000Z
LAST-MODIFIED:20210731T015814Z
UID:4750-1628852400-1628856000@www.ibs.re.kr
SUMMARY:Bayesian model calibration and sensitivity analysis for oscillating biochemical experiments
DESCRIPTION:Abstract: Most organisms exhibit various endogenous oscillating behaviors\, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments\, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments\, however\, poses a significant statistical challenge due to its ill behavior in high dimension\, identifiability\, and numerical instability. This article devises a new Bayesian calibration strategy for oscillating biochemical models. The proposed methodology can efficiently estimate the computer experiments’ (ODE) parameters that match the physical experiments. The proposed framework is illustrated with circadian oscillations observed in a model filamentous fungus\, Neurospora crassa.
URL:https://www.ibs.re.kr/bimag/event/2021-08-13/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/07/HJK_profile-e1626653369732.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210813T090000
DTEND;TZID=Asia/Seoul:20210813T100000
DTSTAMP:20260424T232019
CREATED:20210810T220000Z
LAST-MODIFIED:20210811T064522Z
UID:4837-1628845200-1628848800@www.ibs.re.kr
SUMMARY:TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data
DESCRIPTION:We will discuss about “TimeCycle: Topology Inspired MEthod for the Detection of Cycling Transcripts in Circadian Time-Series Data”\, Ness-Cohn and Braun\, Bioinformatics\, 2021 \nAbstract \nMotivation: The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics\, coupled with the significant implicatins of the circadian clock for human health\, has sparked an interest in circadian profiling studies to discover genes under circadian control.\nResult: We present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series\, the method reconstructs the state space using time-delay embedding\, a data transformation technique from dynamical systems theory. In the embedded space\, Takens’ theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology\, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution\, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle’s ability to identify cycling genes across a range of sampling schemes\, number of replicates\, and missing data. Comparison to competing methods highlights their relative strengths\, providing guidance as to the optimal choice of cycling detection method.\nAvailability: A fully documented open-source R package implementing TimeCycle is available at: https://nesscoder.github.io/TimeCycle/ .
URL:https://www.ibs.re.kr/bimag/event/2021-08-13-2/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210806T130000
DTEND;TZID=Asia/Seoul:20210806T140000
DTSTAMP:20260424T232019
CREATED:20210801T140652Z
LAST-MODIFIED:20210801T140652Z
UID:4835-1628254800-1628258400@www.ibs.re.kr
SUMMARY:Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation
DESCRIPTION:We will discuss about “Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation”\, Li et. al.\, Cell Systems\, 2018 \nAbstract \nGene regulation is a complex non-equilibrium process. Here\, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive\, active\, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First\, for transcription factors (TFs) that regulate transcription burst frequency\, as opposed to amplitude or duration\, weak TF binding is sufficient to elicit strong transcriptional responses. Second\, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural\, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further\, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates\, not different TF occupancy\, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total\, our work demonstrates the relevance of a kinetic\, non-equilibrium framework for understanding transcriptional regulation.
URL:https://www.ibs.re.kr/bimag/event/2021-08-06/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210730T130000
DTEND;TZID=Asia/Seoul:20210730T140000
DTSTAMP:20260424T232019
CREATED:20210726T125859Z
LAST-MODIFIED:20210726T125859Z
UID:4785-1627650000-1627653600@www.ibs.re.kr
SUMMARY:Stochastic reaction networks in dynamic compartment populations
DESCRIPTION:We will discuss about “Stochastic reaction networks in dynamic compartment populations”\, Duso and Zechner\, PNAS\, 2020 \nAbstract: Compartmentalization of biochemical processes underlies all biological systems\, from the organelle to the tissue scale. Theoretical models to study the interplay between noisy reaction dynamics and compartmentalization are sparse\, and typically very challenging to analyze computationally. Recent studies have made progress toward addressing this problem in the context of specific biological systems\, but a general and sufficiently effective approach remains lacking. In this work\, we propose a mathematical framework based on counting processes that allows us to study dynamic compartment populations with arbitrary interactions and internal biochemistry. We derive an efficient description of the dynamics in terms of differential equations which capture the statistics of the population. We demonstrate the relevance of our approach by analyzing models inspired by different biological processes\, including subcellular compartmentalization and tissue homeostasis.
URL:https://www.ibs.re.kr/bimag/event/2021-07-30/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210728T170000
DTEND;TZID=Asia/Seoul:20210728T180000
DTSTAMP:20260424T232019
CREATED:20210407T040301Z
LAST-MODIFIED:20210717T235315Z
UID:4383-1627491600-1627495200@www.ibs.re.kr
SUMMARY:Theory and design of molecular integral feedback controllers
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234)\nAbstract: \nHomeostasis is a recurring theme in biology that ensures that regulated variables robustly adapt to environmental perturbations. This robust perfect adaptation feature is achieved in natural circuits by using integral control\, a negative feedback strategy that performs mathematical integration to achieve structurally robust regulation. Despite its benefits\, the synthetic realization of integral feedback in living cells has remained elusive owing to the complexity of the required biological computations. In this talk I will show that there is a single fundamental biomolecular controller topology that realizes integral feedback and achieves robust perfect adaptation in arbitrary intracellular networks with noisy dynamics. This adaptation property is guaranteed both for the population-average and for the time-average of single cells. On the basis of this concept\, I will describe a genetically engineered synthetic integral feedback controller in living cells and demonstrate its tunability and adaptation properties. A growth-rate control application in Escherichia coli shows the intrinsic capacity of our integral controller to deliver robustness and highlights its potential use as a versatile controller for regulation of biological variables in uncertain networks. These results provide conceptual and practical tools in the area of cybergenetics\, for engineering synthetic controllers that steer the dynamics of living systems.
URL:https://www.ibs.re.kr/bimag/event/2021-07-28/
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/2021/04/MustafaKhammash_profile-e1617768310550.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210723T150000
DTEND;TZID=Asia/Seoul:20210723T160000
DTSTAMP:20260424T232019
CREATED:20210629T013222Z
LAST-MODIFIED:20210629T013222Z
UID:4686-1627052400-1627056000@www.ibs.re.kr
SUMMARY:Scalable Modeling Approaches in Systems Immunology
DESCRIPTION:Abstract: \nSystems biology seeks to build quantitative predictive models of biological system behavior. Biological systems\, such as the mammalian immune system\, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus\, mechanistic\, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with the high-dimensional parameter space arising when building reasonably detailed models. Another challenge is to devise integrated frameworks incorporating behavioral characteristics manifested at various organizational levels seamlessly. First\, we aimed to understand how cell-to-cell heterogeneities are regulated through gene expression variations and their propagation at the single-cell level. To better understand detailed gene regulatory circuit models with many parameters without analytical solutions\, we developed a framework called MAchine learning of Parameter-Phenotype Analysis (MAPPA). MAPPA combines machine learning approaches and stochastic simulation methods to dissect the mapping between high-dimensional parameters and phenotypes. MAPPA elucidated regulatory features of stochastic gene-gene correlation phenotypes. Next\, we sought to quantitatively dissect immune homeostasis conferring tolerance to self-antigens and responsiveness to foreign antigens. Towards this goal\, we built a series of models spanning from intracellular to organismal levels to describe the recurrent reciprocal relationships between self-reactive T cells and regulatory T cells in collaboration with an experimentalist. This effort elucidated critical immune parameters regulating the circuitry enabling the robust suppression of self-reactive T cells\, followed by experimental validation. Moreover\, by bridging these models across organizational scales\, we derived a framework describing immune homeostasis as a dynamical equilibrium between self-activated T cells and regulatory T cells\, typically operating well below thresholds that could result in clonal expansion and subsequent autoimmune diseases. We propose that our framework and predictions may help guide therapeutic manipulation of immune homeostasis to treat cancer and autoimmune diseases. \n  \nReferences: \nPark\, K.\, Prüstel\, T.\, Lu\, Y.\, and Tsang\, J.S. (2019). Machine learning of stochastic gene network phenotypes. BioRxiv 825943. \nWong\, H.S.\, Park\, K.\, Gola\, A.\, Baptista\, A.P.\, Miller\, C.H.\, Deep\, D.\, Lou\, M.\, Boyd\, L.F.\, Rudensky\, A.Y.\, Savage\, P.A.\, et al. (2021). A local regulatory T cell feedback circuit maintains immune homeostasis by pruning self-activated T cells. Cell S0092867421006589.
URL:https://www.ibs.re.kr/bimag/event/2021-07-23/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210723T110000
DTEND;TZID=Asia/Seoul:20210723T120000
DTSTAMP:20260424T232019
CREATED:20210707T160416Z
LAST-MODIFIED:20210707T160416Z
UID:4715-1627038000-1627041600@www.ibs.re.kr
SUMMARY:Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC
DESCRIPTION:Abstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study\, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model\, one of the pharmacokinetic models\, to capture bi-exponential drug decay in plasma. A stochastic process is used to account for system randomness\, and this process is transformed into system of stochastic differential equations. Parameter inference is performed by Approximation Bayesian Computation using the likelihood-free method. Using these collected samples\, global sensitivity of parameters is compared to Uniform and Normal distributions. This approach in the TMDD model may improve model robustness without changing the global sensitivity of parameters and the model.
URL:https://www.ibs.re.kr/bimag/event/inference-method-for-a-stochastic-target-mediated-drug-disposition-model-via-abc-mcmc/
LOCATION:B305 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210722T130000
DTEND;TZID=Asia/Seoul:20210722T140000
DTSTAMP:20260424T232019
CREATED:20210721T190000Z
LAST-MODIFIED:20210726T125353Z
UID:4754-1626958800-1626962400@www.ibs.re.kr
SUMMARY:Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter
DESCRIPTION:We will discuss about “Parameter Estimation in a Model of the Human Circadian Pacemaker Using a Particle Filter”\, Bonarius et. al.\, IEEE Trans. Biomed. Eng.\, 2021 \nAbstract \nObjective: In the near future\, real-time estimation of peoples unique\, precise circadian clock state has the potential to improve the efficacy of medical treatments and improve human performance on a broad scale. Humancentric lighting can bring circadian-rhythm support using biodynamic lighting solutions that sync light with the time of day. We investigate a method to improve the tracking of individual’s circadian processes. Methods: In literature\, the human circadian physiology has been mathematically modeled using ordinary differential equations\, the state of which can be tracked via the signal processing concept of a Particle Filter. We show that substantial improvements can be made if the estimator not only tracks state variables\, such as the phase and amplitude of the circadian pacemaker\, but also fits specific model parameters to the individual. In particular\, we optimize model parameter τx\, which reflects the intrinsic period of the circadian pacemaker (τ). We show that both state and model parameters can be estimated based on minimally-invasive light exposure measurements and sleep-wake state observations. We also quantify the effect of inaccuracies in sensing. Results: We demonstrate improved performance by estimating τx for every individual\, both with artificially created and human subject data. Prediction accuracy improves with every newly available observation. The estimated τx-s correlate well with the subjects’ chronotypes\, in a similar way as τ correlates. Conclusion: Our results show that individualizing the estimation of model parameters can improve circadian state estimation accuracy. Significance: These findings underscore the potential improvements in personalized models over one-size fits all approaches.
URL:https://www.ibs.re.kr/bimag/event/2021-07-22/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210715T130000
DTEND;TZID=Asia/Seoul:20210715T140000
DTSTAMP:20260424T232019
CREATED:20210713T071946Z
LAST-MODIFIED:20210715T002734Z
UID:4721-1626354000-1626357600@www.ibs.re.kr
SUMMARY:Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions
DESCRIPTION:We will discuss about “Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions”\, Thurley et al.\, Cell Systems\, 2021 \nAbstract: \nCell-to-cell communication networks have critical roles in coordinating diverse organismal processes\, such as tissue development or immune cell response. However\, compared with intracellular signal transduction networks\, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here\, we make use of a framework that treats intracellular signal transduction networks as “black boxes” with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time\, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.
URL:https://www.ibs.re.kr/bimag/event/2021-07-15/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210714T170000
DTEND;TZID=Asia/Seoul:20210714T180000
DTSTAMP:20260424T232019
CREATED:20210406T074701Z
LAST-MODIFIED:20210420T215116Z
UID:4368-1626282000-1626285600@www.ibs.re.kr
SUMMARY:Inference for Circadian Pacemaking
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Organisms have evolved an internal biological clock which allows them to temporally regulate and organize their physiological and behavioral responses to cope in an optimal way with the fundamentally periodic nature of the environment. It is now well established that the molecular genetics of such rhythms within the cell consist of interwoven transcriptional-translational feedback loops involving about 15 clock genes\, which generate circa 24-h oscillations in many cellular functions at cell population or whole organism levels. We will present statistical methods and modelling approaches that address newly emerging large circadian data sets\, namely spatio-temporal gene expression in SCN neurons and rest-activity actigraph data obtained from non-invasive e-monitoring\, both of which provide unique opportunities for furthering progress in understanding the synchronicity of circadian pacemaking and address implications for monitoring patients in chronotherapeutic healthcare.
URL:https://www.ibs.re.kr/bimag/event/2021-07-14/
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/2021/04/barbel_finkenstadt_rand_crop-e1617768405446.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR