BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20211007T110000
DTEND;TZID=Asia/Seoul:20211007T120000
DTSTAMP:20260510T021710
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:20211008T140000
DTEND;TZID=Asia/Seoul:20211008T150000
DTSTAMP:20260510T021710
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:20211021T110000
DTEND;TZID=Asia/Seoul:20211021T120000
DTSTAMP:20260510T021710
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:20211022T130000
DTEND;TZID=Asia/Seoul:20211022T140000
DTSTAMP:20260510T021710
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:20211027T110000
DTEND;TZID=Asia/Seoul:20211027T120000
DTSTAMP:20260510T021710
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:20211027T170000
DTEND;TZID=Asia/Seoul:20211027T180000
DTSTAMP:20260510T021710
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:20211029T150000
DTEND;TZID=Asia/Seoul:20211029T170000
DTSTAMP:20260510T021710
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
END:VCALENDAR