BEGIN:VCALENDAR
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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
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:20210902T100000
DTEND;TZID=Asia/Seoul:20210902T110000
DTSTAMP:20260510T051141
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:20210902T130000
DTEND;TZID=Asia/Seoul:20210902T140000
DTSTAMP:20260510T051141
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:20210908T170000
DTEND;TZID=Asia/Seoul:20210908T180000
DTSTAMP:20260510T051141
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:20210909T090000
DTEND;TZID=Asia/Seoul:20210909T100000
DTSTAMP:20260510T051141
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:20210909T110000
DTEND;TZID=Asia/Seoul:20210909T120000
DTSTAMP:20260510T051141
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:20210916T110000
DTEND;TZID=Asia/Seoul:20210916T120000
DTSTAMP:20260510T051141
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:20210917T130000
DTEND;TZID=Asia/Seoul:20210917T140000
DTSTAMP:20260510T051141
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:20210924T130000
DTEND;TZID=Asia/Seoul:20210924T140000
DTSTAMP:20260510T051141
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
END:VCALENDAR