BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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:20210917T130000
DTEND;TZID=Asia/Seoul:20210917T140000
DTSTAMP:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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:20260425T063242
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
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210712T100000
DTEND;TZID=Asia/Seoul:20210712T120000
DTSTAMP:20260425T063242
CREATED:20210617T030615Z
LAST-MODIFIED:20210617T030615Z
UID:4658-1626084000-1626091200@www.ibs.re.kr
SUMMARY:Analysis of sleep-wake cycles via machine learning and mathematical modeling
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-07-21/
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:20210709T130000
DTEND;TZID=Asia/Seoul:20210709T140000
DTSTAMP:20260425T063242
CREATED:20210705T061640Z
LAST-MODIFIED:20210705T131643Z
UID:4710-1625835600-1625839200@www.ibs.re.kr
SUMMARY:DeepCME: A deep learning framework for solving the Chemical Master Equation
DESCRIPTION:We will discuss about “DeepCME: A deep learning framework for solving the Chemical Master Equation\,” Gupta et al.\, bioRxiv\, 2021 \nStochastic models of biomolecular reaction networks are commonly employed in systems and synthetic biology to study the effects of stochastic fluctuations emanating from reactions involving species with low copy-numbers. For such models\, the Kolmogorov’s forward equation is called the chemical master equation (CME)\, and it is a fundamental system of linear ordinary differential equations (ODEs) that describes the evolution of the probability distribution of the random state-vector representing the copy-numbers of all the reacting species. The size of this system is given by the number of states that are accessible by the chemical system\, and for most examples of interest this number is either very large or infinite. Moreover\, approximations that reduce the size of the system by retaining only a finite number of important chemical states (e.g. those with non-negligible probability) result in high-dimensional ODE systems\, even when the number of reacting species is small. Consequently\, accurate numerical solution of the CME is very challenging\, despite the linear nature of the underlying ODEs. One often resorts to estimating the solutions via computationally intensive stochastic simulations. The goal of the present paper is to develop a novel deep-learning approach for solving high-dimensional CMEs by reformulating the stochastic dynamics using Kolmogorov’s backward equation. The proposed method leverages superior approximation properties of Deep Neural Networks (DNNs) and is algorithmically based on reinforcement learning. It only requires a moderate number of stochastic simulations (in comparison to typical simulation-based approaches) to train the “policy function”. This allows not just the numerical approximation of the CME solution but also of its sensitivities to all the reaction network parameters (e.g. rate constants). We provide four examples to illustrate our methodology and provide several directions for future research.
URL:https://www.ibs.re.kr/bimag/event/2021-07-09/
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:20210702T120000
DTEND;TZID=Asia/Seoul:20210702T130000
DTSTAMP:20260425T063242
CREATED:20210507T124154Z
LAST-MODIFIED:20210622T234621Z
UID:4550-1625227200-1625230800@www.ibs.re.kr
SUMMARY:Collective Oscillations in coupled cell systems
DESCRIPTION:We will discuss about “Collective Oscillations in coupled cell systems”\, Chen and Sinh\, Bulletin of Mathematical Biology\, 2021 \nWe investigate oscillations in coupled systems. The methodology is based on the Hopf bifurcation theorem and a condition extended from the Routh–Hurwitz criterion. Such a condition leads to locating the bifurcation values of the parameters. With such an approach\, we analyze a single-cell system modeling the minimal genetic negative feedback loop and the coupled-cell system composed by these single-cell systems. We study the oscillatory properties for these systems and compare these properties between the model with Hill-type repression and the one with protein-sequestration-based repression. As the parameters move from the Hopf bifurcation value for single cells to the one for coupled cells\, we compute the eigenvalues of the linearized systems to obtain the magnitude of the collective frequency when the periodic solution of the coupled-cell system is generated. Extending from this information on the parameter values\, we further compute and compare the collective frequency for the coupled-cell system and the average frequency of the decoupled individual cells. To compare these scenarios with other biological oscillators\, we perform parallel analysis and computations on a segmentation clock model.
URL:https://www.ibs.re.kr/bimag/event/2021-07-02/
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:20210701T110000
DTEND;TZID=Asia/Seoul:20210701T120000
DTSTAMP:20260425T063242
CREATED:20210603T003009Z
LAST-MODIFIED:20210604T082929Z
UID:4605-1625137200-1625140800@www.ibs.re.kr
SUMMARY:Statistical Inference with Neural Network Imputation for Item Nonresponse
DESCRIPTION:Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects\, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation\, including variance estimation\, is challenging because the basis for function estimation is estimated rather than known. In this paper\, we tackle the problem of statistical inference with neural network imputation by treating the hidden nodes in a neural network as data-driven basis functions. We prove that the uncertainty in estimating the basis functions can be safely ignored and hence the linearization method for neural network imputation can be greatly simplified. A simulation study confirms that the proposed approach results in efficient and well-calibrated confidence intervals even when classic approaches fail due to severe nonlinearity and complicated interactions.
URL:https://www.ibs.re.kr/bimag/event/2021-07-01/
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/2021/06/JKK_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210629T130000
DTEND;TZID=Asia/Seoul:20210629T140000
DTSTAMP:20260425T063242
CREATED:20210607T235505Z
LAST-MODIFIED:20210607T235505Z
UID:4627-1624971600-1624975200@www.ibs.re.kr
SUMMARY:Deciphering circadian clock cell network morphology within the biological master clock\, the suprachiasmatic nucleus
DESCRIPTION:Abstract: The biological master clock\, the suprachiasmatic nucleus (SCN) of a mouse\, contains many (~20\,000) clock cells heterogeneous\, particularly with respect to their circadian period. Despite the inhomogeneity\, within an intact SCN\, they maintain a very high degree of circadian phase coherence\, which is generally rendered visible as system-wide propagating phase waves. The phase coherence is vital for mammals sustaining various circadian rhythmic activities. It is supposedly achieved not by one but a few different cell-to-cell coupling mechanisms: Among others\, action potential (AP)-mediated connectivity is known to be essential. However\, due to technical difficulties and limitations in experiments\, so far\, very little information is available about the (connectome) morphology of the AP-mediated SCN neural connectivity. With that limited amount of information\, here we exhaustively and systematically explore a large (~25\,000) pool of various model network morphologies to come up with the most realistic case for the SCN. All model networks within this pool reflect an actual indegree distribution as well as a physical range distribution of afferent clock cells\, which were acquired in earlier optogenetic connectome experiments. Subsequently\, our network selection scheme is based on a collection of multitude criteria\, testing the properties of SCN circadian phase waves in perturbed (or driven) as well as in their natural states: Key properties include\, 1) degree of phase synchrony (or dispersal) and direction of wave propagation\, 2) entrainability of the model oscillator networks to an external circadian forcing (mimicking the light modulation subject to the geophysical circadian rhythm)\, and 3) emergence of “phase-singularities” following a global perturbation and their decay. The selected network morphologies require several common features that 1) the indegree – outdegree relation must have a positive correlation; 2) the cells in the SCN core region have a larger total (in+out) degree than that of the shell region; 3) core to shell (or shell to core) connections should be much less than core to core (and shell to shell) connections. Taken all together\, our comprehensive test results strongly suggest that degree distribution over the whole SCN is not uniform but position-dependent and raise a question of whether this inhomogeneous degree distribution is related to the distribution of known subpopulations of SCN cells.
URL:https://www.ibs.re.kr/bimag/event/deciphering-circadian-clock-cell-network-morphology-within-the-biological-master-clock-the-suprachiasmatic-nucleus/
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:20210618T130000
DTEND;TZID=Asia/Seoul:20210618T140000
DTSTAMP:20260425T063242
CREATED:20210608T152356Z
LAST-MODIFIED:20210612T121922Z
UID:4635-1624021200-1624024800@www.ibs.re.kr
SUMMARY:Introduction to immersed boundary method for biofluids
DESCRIPTION:Abstract: TBA
URL:https://www.ibs.re.kr/bimag/event/2021-06-18-2/
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/2021/02/SookkyungLim-e1706058905732.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210611T123000
DTEND;TZID=Asia/Seoul:20210611T133000
DTSTAMP:20260425T063242
CREATED:20210507T123416Z
LAST-MODIFIED:20210601T035036Z
UID:4545-1623414600-1623418200@www.ibs.re.kr
SUMMARY:DNA as a universal substrate for chemical kinetics
DESCRIPTION:We will discuss about “DNA as a universal substrate for chemical kinetics “\, Soloveichik et al.\, PNAS (2009) \nMolecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive\, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously\, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator\, a limit-cycle oscillator\, a chaotic system\, and systems implementing feedback digital logic and algorithmic behavior.
URL:https://www.ibs.re.kr/bimag/event/2021-05-27/
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:20210610T110000
DTEND;TZID=Asia/Seoul:20210610T120000
DTSTAMP:20260425T063242
CREATED:20210406T074242Z
LAST-MODIFIED:20210607T080017Z
UID:4364-1623322800-1623326400@www.ibs.re.kr
SUMMARY:Towards individualized predictions of human sleep and circadian timing
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Accurate assessment of circadian timing is critical to many applications\, including timing of drug delivery\, prediction of neurobehavioral performance\, and optimized scheduling of sleep. Current methods for measuring circadian timing are onerous and do not produce results in real time. Mathematical models have been developed for predicting circadian timing from an individual’s light exposure patterns\, which can be applied to passively collected data. These models are now well validated in the field at the group-average level\, but tend to perform poorly at the individual level. One potential solution to this problem is the estimation of model parameters at an individual level. We explored whether this approach could be applied to parameters relating to an individual’s light sensitivity. We found that these parameters can account for inter-individual and intra-individual variation in circadian timing. These findings demonstrate that model parametrization based on physiological measurements of light sensitivity could lead to more accurate individual-level circadian phase prediction.
URL:https://www.ibs.re.kr/bimag/event/2021-06-10/
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/AndrewPhillips_profile_crop-e1617768455279.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210526T170000
DTEND;TZID=Asia/Seoul:20210526T180000
DTSTAMP:20260425T063242
CREATED:20210311T114629Z
LAST-MODIFIED:20210407T040940Z
UID:4248-1622048400-1622052000@www.ibs.re.kr
SUMMARY:Neural network aided approximation and parameter inference of stochastic models of gene expression
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Non-Markov models of stochastic biochemical kinetics often incorporate explicit time delays to effectively model large numbers of intermediate biochemical processes. Analysis and simulation of these models\, as well as the inference of their parameters from data\, are fraught with difficulties because the dynamics depends on the system’s history. Here we use an artificial neural network to approximate the time-dependent distributions of non-Markov models by the solutions of much simpler time-inhomogeneous Markov models; the approximation does not increase the dimensionality of the model and simultaneously leads to inference of the kinetic parameters. The training of the neural network uses a relatively small set of noisy measurements generated by experimental data or stochastic simulations of the non-Markov model. We show using a variety of models\, where the delays stem from transcriptional processes and feedback control\, that the Markov models learnt by the neural network accurately reflect the stochastic dynamics across parameter space.
URL:https://www.ibs.re.kr/bimag/event/2021-05-26/
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/2021/03/DjvWsbfJ-e1617756286824.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210520T123000
DTEND;TZID=Asia/Seoul:20210520T133000
DTSTAMP:20260425T063242
CREATED:20210507T123654Z
LAST-MODIFIED:20210507T123746Z
UID:4547-1621513800-1621517400@www.ibs.re.kr
SUMMARY:Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes
DESCRIPTION:We will discuss about “Independent Markov Decomposition: Towards modeling kinetics of biomolecular complexes”\, Hempel et. al.\, bioRxiv\, 2021 \nIn order to advance the mission of in silico cell biology\, modeling the interactions of large and complex biological systems becomes increasingly relevant. The combination of molecular dynamics (MD) and Markov state models (MSMs) have enabled the construction of simplified models of molecular kinetics on long timescales. Despite its success\, this approach is inherently limited by the size of the molecular system. With increasing size of macromolecular complexes\, the number of independent or weakly coupled subsystems increases\, and the number of global system states increase exponentially\, making the sampling of all distinct global states unfeasible. In this work\, we present a technique called Independent Markov Decomposition (IMD) that leverages weak coupling between subsystems in order to compute a global kinetic model without requiring to sample all combinatorial states of subsystems. We give a theoretical basis for IMD and propose an approach for finding and validating such a decomposition. Using empirical few-state MSMs of ion channel models that are well established in electrophysiology\, we demonstrate that IMD can reproduce experimental conductance measurements with a major reduction in sampling compared with a standard MSM approach. We further show how to find the optimal partition of all-atom protein simulations into weakly coupled subunits.
URL:https://www.ibs.re.kr/bimag/event/2021-05-20/
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:20210514T110000
DTEND;TZID=Asia/Seoul:20210514T120000
DTSTAMP:20260425T063242
CREATED:20210507T124508Z
LAST-MODIFIED:20210507T124508Z
UID:4555-1620990000-1620993600@www.ibs.re.kr
SUMMARY:Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model
DESCRIPTION:We will discuss about “Extending Transfer Entropy Improves Identification of Effective Connectivity in a Spiking Cortical Network Model”\, Ito et. al.\, PloS ONE\, 2011 \nTransfer entropy (TE) is an information-theoretic measure which has received recent attention in neuroscience for its potential to identify effective connectivity between neurons. Calculating TE for large ensembles of spiking neurons is computationally intensive\, and has caused most investigators to probe neural interactions at only a single time delay and at a message length of only a single time bin. This is problematic\, as synaptic delays between cortical neurons\, for example\, range from one to tens of milliseconds. In addition\, neurons produce bursts of spikes spanning multiple time bins. To address these issues\, here we introduce a free software package that allows TE to be measured at multiple delays and message lengths. To assess performance\, we applied these extensions of TE to a spiking cortical network model (Izhikevich\, 2006) with known connectivity and a range of synaptic delays. For comparison\, we also investigated single-delay TE\, at a message length of one bin (D1TE)\, and cross-correlation (CC) methods. We found that D1TE could identify 36% of true connections when evaluated at a false positive rate of 1%. For extended versions of TE\, this dramatically improved to 73% of true connections. In addition\, the connections correctly identified by extended versions of TE accounted for 85% of the total synaptic weight in the network. Cross correlation methods generally performed more poorly than extended TE\, but were useful when data length was short. A computational performance analysis demonstrated that the algorithm for extended TE\, when used on currently available desktop computers\, could extract effective connectivity from 1 hr recordings containing 200 neurons in ∼5 min. We conclude that extending TE to multiple delays and message lengths improves its ability to assess effective connectivity between spiking neurons. These extensions to TE soon could become practical tools for experimentalists who record hundreds of spiking neurons.
URL:https://www.ibs.re.kr/bimag/event/2021-05-14/
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:20210507T123000
DTEND;TZID=Asia/Seoul:20210507T133000
DTSTAMP:20260425T063242
CREATED:20210503T075749Z
LAST-MODIFIED:20210503T075749Z
UID:4525-1620390600-1620394200@www.ibs.re.kr
SUMMARY:Introduction to Bayesian ML/DL\, with Application to Parameter Inference of Coupled Non-linear ODEs - Part 2
DESCRIPTION:In this talk\, the speaker will present introductory materials about Bayesian Machine Learning. \nAbstract\nThe problem of approximating the posterior distribution (or density estimation in general) is a crucial problem in Bayesian statistics\, in which intractable integrals often become the computational bottleneck. MCMC sampling is the most widely used family of algorithms for approximating posteriors. However\, if the underlying graphical model is too complex or the data is in very high dimensions\, then such sampling-based methodologies run into several problems. Variational inference (Jordan et al.\, 1999; Wainwright and Jordan\, 2008) is a family of machine learning methodologies that transforms the problem of approximating posterior densities to an optimization\, which lets us circumvent all such problems. In the first part\, I will introduce the general framework of variational inference and some underlying theory\, accompanied by an illustrative example of LDA (Blei et al.\, 2003). In the second part\, I will introduce some recent works on applying variational inference to parameter inference of coupled non-linear ODEs arising in various biological contexts.
URL:https://www.ibs.re.kr/bimag/event/introduction-to-bayesian-ml-dl-with-application-to-parameter-inference-of-coupled-non-linear-odes-part-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
END:VCALENDAR