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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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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230512T110000
DTEND;TZID=Asia/Seoul:20230512T130000
DTSTAMP:20260425T195229
CREATED:20230430T155858Z
LAST-MODIFIED:20230508T134254Z
UID:7653-1683889200-1683896400@www.ibs.re.kr
SUMMARY:Hyukpyo Hong\, Inference and uncertainty quantification of stochastic gene expression via synthetic models
DESCRIPTION:We will discuss about “Inference and uncertainty quantification of stochastic gene expression via synthetic models”\, Öcal et al.\, J. R. Soc. Interface. \nAbstract \n\n\n\n\nEstimating uncertainty in model predictions is a central task in quantitativebiology. Biological models at the single-cell level are intrinsically stochastic and nonlinear\, creating formidable challenges for their statistical estimation which inevitably has to rely on approximations that trade accuracy for tractability. Despite intensive interest\, a sweet spot in this trade-off has not been found yet. We propose a flexible procedure for uncertainty quantification in a wide class of reaction networks describing stochastic gene expression including those with feedback. The method is based on creating a tractable coarse-graining of the model that is learned from simulations\, a synthetic model\, to approximate the likelihood function. We demonstrate that synthetic models can substantially outperform state-of-the-art approaches on a number of non-trivial systems and datasets\, yielding an accurate and computationally viable solution to uncertainty quantification in stochastic models of gene expression.
URL:https://www.ibs.re.kr/bimag/event/2023-05-12-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:20230519T140000
DTEND;TZID=Asia/Seoul:20230519T160000
DTSTAMP:20260425T195229
CREATED:20230430T033701Z
LAST-MODIFIED:20230515T040214Z
UID:7648-1684504800-1684512000@www.ibs.re.kr
SUMMARY:Dongju Lim\, A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease
DESCRIPTION:We will discuss about “A multi-scale model explains oscillatory slowing and neuronal hyperactivity in Alzheimer’s disease”\, Alexandersen\, Christoffer G.\, et al.\, Journal of the Royal Society Interface 20.198 (2023): 20220607. \nAbstract \n\n\n\n\n\n\nAlzheimer’s disease is the most common cause of dementia and is linked to the spreading of pathological amyloid-β and tau proteins throughout the brain. Recent studies have highlighted stark differences in how amyloid-β and tau affect neurons at the cellular scale. On a larger scale\, Alzheimer’s patients are observed to undergo a period of early-stage neuronal hyperactivation followed by neurodegeneration and frequency slowing of neuronal oscillations. Herein\, we model the spreading of both amyloid-β and tau across a human connectome and investigate how the neuronal dynamics are affected by disease progression. By including the effects of both amyloid-β and tau pathology\, we find that our model explains AD-related frequency slowing\, early-stage hyperactivation and late-stage hypoactivation. By testing different hypotheses\, we show that hyperactivation and frequency slowing are not due to the topological interactions between different regions but are mostly the result of local neurotoxicity induced by amyloid-β and tau protein.
URL:https://www.ibs.re.kr/bimag/event/2023-05-19-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:20230526T140000
DTEND;TZID=Asia/Seoul:20230526T160000
DTSTAMP:20260425T195229
CREATED:20230430T034034Z
LAST-MODIFIED:20230524T094243Z
UID:7650-1685109600-1685116800@www.ibs.re.kr
SUMMARY:Hyeontae Jo\,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning
DESCRIPTION:We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”\, Zhao\, Shuai\, et al.\, IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. \nAbstract \nPhysics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics\, this article proposes a PIML-based parameter estimation method demonstrated by a case study of dc–dc Buck converter. A deep neural network and the dynamic models of the converter are seamlessly coupled. It overcomes the challenges related to training data\, accuracy\, and robustness which a typical data-driven approach has. This exemplary application envisions to provide a new perspective for tailoring existing machine learning tools for power electronics.
URL:https://www.ibs.re.kr/bimag/event/2023-05-26-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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