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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:20210101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220902T150000
DTEND;TZID=Asia/Seoul:20220902T160000
DTSTAMP:20260426T100536
CREATED:20220817T042800Z
LAST-MODIFIED:20220828T171528Z
UID:6398-1662130800-1662134400@www.ibs.re.kr
SUMMARY:Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data
DESCRIPTION:We will discuss about “Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data”\, Huang\, Qi\, Journal of The Royal Society Interface 15.139 (2018): 20170885. \nAbstract: Wearable computing devices allow collection of densely sampled real-time information on movement enabling researchers and medical experts to obtain objective and non-obtrusive records of actual activity of a subject in the real world over many days. Our interest here is motivated by the use of activity data for evaluating and monitoring the circadian rhythmicity of subjects for research in chronobiology and chronotherapeutic healthcare. In order to translate the information from such high-volume data arising we propose the use of a Markov modelling approach which (i) naturally captures the notable square wave form observed in activity data along with heterogeneous ultradian variances over the circadian cycle of human activity\, (ii) thresholds activity into different states in a probabilistic way while respecting time dependence and (iii) gives rise to circadian rhythm parameter estimates\, based on probabilities of transitions between rest and activity\, that are interpretable and of interest to circadian research.
URL:https://www.ibs.re.kr/bimag/event/2022-09-02-jc/
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:20220916T110000
DTEND;TZID=Asia/Seoul:20220916T120000
DTSTAMP:20260426T100536
CREATED:20220825T190000Z
LAST-MODIFIED:20220905T053032Z
UID:6351-1663326000-1663329600@www.ibs.re.kr
SUMMARY:Physics-informed neural networks for PDE-constrained optimization and control
DESCRIPTION:We will discuss about “Physics-informed neural networks for PDE-constrained optimization and control”\, Barry-Straume\, Jostein\, et al.\, arXiv preprint arXiv:2205.03377 (2022). \nAbstract: A fundamental problem of science is designing optimal control policies that manipulate a given environment into producing a desired outcome. Control PhysicsInformed Neural Networks simultaneously solve a given system state\, and its respective optimal control\, in a one-stage framework that conforms to physical laws of the system. Prior approaches use a two-stage framework that models and controls a system sequentially\, whereas Control PINNs incorporates the required optimality conditions in its architecture and loss function. The success of Control PINNs is demonstrated by solving the following open-loop optimal control problems: (i) an analytical problem (ii) a one-dimensional heat equation\, and (iii) a two-dimensional predator-prey problem.
URL:https://www.ibs.re.kr/bimag/event/2022-09-16-jc/
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:20220923T150000
DTEND;TZID=Asia/Seoul:20220923T160000
DTSTAMP:20260426T100536
CREATED:20220830T011634Z
LAST-MODIFIED:20220922T011820Z
UID:6529-1663945200-1663948800@www.ibs.re.kr
SUMMARY:Cell clustering for spatial transcriptomics data with graph neural networks
DESCRIPTION:We will discuss about “Cell clustering for spatial transcriptomics data with graph neural networks”\, Li\, J.\, Chen\, S.\, Pan\, X. et al.\, Nat Comput Sci 2\, 399–408 (2022) \nAbstract: \nSpatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks\, we introduce cell clustering for spatial transcriptomics data with graph neural networks\, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets\, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain\, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments\, which are all validated experimentally\, inspiring biological hypotheses about the underlying interactions among the cell state\, cell type and micro-environment. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-23/
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:20220930T150000
DTEND;TZID=Asia/Seoul:20220930T160000
DTSTAMP:20260426T100536
CREATED:20220830T012122Z
LAST-MODIFIED:20220830T012141Z
UID:6531-1664550000-1664553600@www.ibs.re.kr
SUMMARY:Absolute concentration robustness in networks with low-dimensional stoichiometric subspace
DESCRIPTION:We will discuss about “Absolute concentration robustness in networks with low-dimensional stoichiometric subspace”\, Meshkat\, Nicolette\, Anne Shiu\, and Angelica Torres.\, Vietnam Journal of Mathematics 50.3 (2022): 623-651. \nAbstract: \nA reaction system exhibits “absolute concentration robustness” (ACR) in some species if the positive steady-state value of that species does not depend on initial conditions. Mathematically\, this means that the positive part of the variety of the steady-state ideal lies entirely in a hyperplane of the form xi = c\, for some c > 0. Deciding whether a given reaction system – or those arising from some reaction network – exhibits ACR is difficult in general\, but here we show that for many simple networks\, assessing ACR is straightforward. Indeed\, our criteria for ACR can be performed by simply inspecting a network or its standard embedding into Euclidean space. Our main results pertain to networks with many conservation laws\, so that all reactions are parallel to one other. Such “one-dimensional” networks include those networks having only one species. We also consider networks with only two reactions\, and show that ACR is characterized by a well-known criterion of Shinar and Feinberg. Finally\, up to some natural ACR-preserving operations – relabeling species\, lengthening a reaction\, and so on – only three families of networks with two reactions and two species have ACR. Our results are proven using algebraic and combinatorial techniques. \n 
URL:https://www.ibs.re.kr/bimag/event/2022-09-30-jc/
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
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