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PRODID:-//Biomedical Mathematics Group - ECPv6.17.4.1//NONSGML v1.0//EN
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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20200101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210610T110000
DTEND;TZID=Asia/Seoul:20210610T120000
DTSTAMP:20210607T080017Z
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:20210407T040940Z
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:20210421T170000
DTEND;TZID=Asia/Seoul:20210421T183000
DTSTAMP:20210421T074343Z
CREATED:20210324T050549Z
LAST-MODIFIED:20210421T074343Z
UID:4307-1619024400-1619029800@www.ibs.re.kr
SUMMARY:Advice to my younger self
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nFacebook live streaming: https://www.facebook.com/10226475900150025/videos/10226475902790091 \nAge brings the benefit of experience and looking back at my job as a professor\, there are a couple of things that fall into the category “I wish someone had told me that earlier”. In this seminar\, I would like to share some of the things I learned and which\, I hope\, will be useful for younger scientists. \nThe questions I will touch upon include \n\n\n\nWhat is productivity\, for a scientist?\nWhat are qualities of successful people?\nHow can one create motivation and success?\nHow to organize myself? (project management; getting things done)\nHow to communicate effectively?\nSeeking fulfillment\n\n\n\nThe seminar is targeted at PhD students\, postdocs\, and junior group leaders. \n \n 
URL:https://www.ibs.re.kr/bimag/event/2021-04-21/
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/olaf-wolkenhauer-e1617756681631.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210415T110000
DTEND;TZID=Asia/Seoul:20210415T120000
DTSTAMP:20210412T021311Z
CREATED:20210314T044747Z
LAST-MODIFIED:20210412T021311Z
UID:4258-1618484400-1618488000@www.ibs.re.kr
SUMMARY:Dynamics-based data science in biology
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Life science has been a prosperous subject for a long time\, and is still developing with high speed now. One of its major aims is to study the mechanisms of various biological processes on the basis of biological big-data. Many statistics-based methods have been proposed to catch the essence by mining those data\, including the popular category classification\, variables regression\, group clustering\, statistical comparison\, dimensionality reduction\, and component analysis\, which\, however\, mainly elucidate static features or steady behavior of living organisms due to lack of temporal data. But\, a biological system is inherently dynamic\, and with increasingly accumulated time-series data\, dynamics-based approaches based on physical and biological laws are demanded to reveal dynamic features or complex behavior of biological systems. In this talk\, I will present a new concept “dynamics-based data science” and the approaches for studying dynamical bio-processes\, including dynamical network biomarkers (DNB)\, autoreservoir neural networks (ARNN) and partical cross-mapping. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamics-based data-driven approaches as explicable\, quantifiable\, and generalizable. In particular\, dynamics-based data science approaches exploit the essential features of dynamical systems in terms of data\, e.g. strong fluctuations near a bifurcation point\, low-dimensionality of a center manifold or an attractor\, and phase-space reconstruction from a single variable by delay embedding theorem\, and thus are able to provide different or additional information to the traditional approaches\, i.e. statistics-based data science approaches. The dynamical-based data science approaches will further play an important role in the systematical research of biology and medicine in future.
URL:https://www.ibs.re.kr/bimag/event/2021-04-15/
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/LC_profile2.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20210325T110000
DTEND;TZID=Asia/Seoul:20210325T120000
DTSTAMP:20210406T075105Z
CREATED:20210301T013812Z
LAST-MODIFIED:20210406T075105Z
UID:4167-1616670000-1616673600@www.ibs.re.kr
SUMMARY:Daniel Forger\, The mathematics of the wearables with applications to circadian rhythms and sleep
DESCRIPTION:This talk will be presented online. Zoom link: 709 120 4849 (pw: 1234) \nAbstract: Millions of individuals track their steps\, heart rate\, and other physiological signals through wearables. This data scale is unprecedented; I will describe several of our apps and ongoing studies\, each of which collects wearable and mobile data from thousands of users\, even in > 100 countries. This data is so noisy that it often seems unusable and in desperate need of new mathematical techniques to extract key signals used in the (ode) mathematical modeling typically done in mathematical biology. I will describe several techniques we have developed to analyze this data and simulate models\, including gap orthogonalized least squares\, a new ansatz for coupled oscillators\, which is similar to the popular ansatz by Ott and Antonsen\, but which gives better fits to biological data and a new level-set Kalman Filter that can be used to simulate population densities. My focus applications will be determining the phase of circadian rhythms\, the scoring of sleep and the detection of COVID with wearables.
URL:https://www.ibs.re.kr/bimag/event/2021-03-25/
LOCATION:ZOOM ID: 709 120 4849 (ibsbimag)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium,Seminar
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2021/03/dannyg.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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