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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
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X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231101T160000
DTEND;TZID=Asia/Seoul:20231101T170000
DTSTAMP:20260524T104732
CREATED:20230831T143129Z
LAST-MODIFIED:20240728T144218Z
UID:8402-1698854400-1698858000@www.ibs.re.kr
SUMMARY:Eder Zavala\, Quantitative analysis of high-resolution daily profiles of HPA axis hormones
DESCRIPTION:Abstract: The Hypothalamic-Pituitary-Adrenal (HPA) axis is the key regulatory pathway responsible for maintaining homeostasis under conditions of real or perceived stress. Endocrine responses to stressors are mediated by adrenocorticotrophic hormone (ACTH) and corticosteroid (CORT) hormones. In healthy\, non-stressed conditions\, ACTH and CORT exhibit highly correlated ultradian pulsatility with an amplitude modulated by circadian processes. Disruption of these hormonal rhythms can occur as a result of stressors or in the very early stages of disease. Despite the fact that misaligned endocrine rhythms are associated with increased morbidity\, a quantitative understanding of their mechanistic origin and pathogenicity is missing. Mathematically\, the HPA axis can be understood as a dynamical system that is optimised to respond and adapt to perturbations. Normally\, the body copes well with minor disruptions\, but finds it difficult to withstand severe\, repeated or long-lasting perturbations. Whilst a healthy HPA axis maintains a certain degree of robustness to stressors\, its fragility in diseased states is largely unknown\, and this understanding constitutes a critical step toward the development of digital tools to support clinical decision-making. This talk will explore how these challenges are being addressed by combining high-resolution biosampling techniques with mathematical and computational analysis methods. This interdisciplinary approach is helping us quantify the inter-individual variability of daily hormone profiles and develop novel “dynamic biomarkers” that serve as a normative reference and to signal endocrine dysfunction. By shifting from a qualitative to a quantitative description of the HPA axis\, these insights bring us a step closer to personalised clinical interventions for which timing is key.
URL:https://www.ibs.re.kr/bimag/event/eder-zavala-quantitative-analysis-of-high-resolution-daily-profiles-of-hpa-axis-hormones/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/08/Eder-Zavala-e1722177727704.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231110T110000
DTEND;TZID=Asia/Seoul:20231110T120000
DTSTAMP:20260524T104732
CREATED:20230831T142922Z
LAST-MODIFIED:20240728T144105Z
UID:8399-1699614000-1699617600@www.ibs.re.kr
SUMMARY:Matthew Simpson\, Efficient prediction\, estimation and identifiability analysis with mechanistic mathematical models
DESCRIPTION:Abstract: Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic\, computationally efficient likelihood-based workflow that addresses all three steps in a unified way. Recently developed methods for constructing profile-wise prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters\, and then combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. We apply our methods to a range of synthetic data and real-world ecological data describing re-growth of coral reefs on the Great Barrier Reef after some external disturbance\, such as a tropical cyclone or coral bleaching event.
URL:https://www.ibs.re.kr/bimag/event/matthew-simpson-efficient-prediction-estimation-and-identifiability-analysis-with-mechanistic-mathematical-models/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/08/Matthew-Simpson-e1722177652995.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231110T140000
DTEND;TZID=Asia/Seoul:20231110T160000
DTSTAMP:20260524T104732
CREATED:20231030T040327Z
LAST-MODIFIED:20231109T000051Z
UID:8661-1699624800-1699632000@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning
DESCRIPTION:We will discuss about “Uncertainty quantified discovery of chemical reaction systems via Bayesian scientific machine learning.” bioRxiv (2023): 2023-09. \n  \nAbstract \nThe recently proposed Chemical Reaction Neural Network (CRNN) discovers chemical reaction pathways from time resolved species concentration data in a deterministic manner. Since the weights and biases of a CRNN are physically interpretable\, the CRNN acts as a digital twin of a classical chemical reaction network. In this study\, we employ a Bayesian inference analysis coupled with neural ordinary differential equations (ODEs) on this digital twin to discover chemical reaction pathways in a probabilistic manner. This allows for estimation of the uncertainty surrounding the learned reaction network. To achieve this\, we propose an algorithm which combines neural ODEs with a preconditioned stochastic gradient langevin descent (pSGLD) Bayesian framework\, and ultimately performs posterior sampling on the neural network weights. We demonstrate the successful implementation of this algorithm on several reaction systems by not only recovering the chemical reaction pathways but also estimating the uncertainty in our predictions. We compare the results of the pSGLD with that of the standard SGLD and show that this optimizer more efficiently and accurately estimates the posterior of the reaction network parameters. Additionally\, we demonstrate how the embedding of scientific knowledge improves extrapolation accuracy by comparing results to purely data-driven machine learning methods. Together\, this provides a new framework for robust\, autonomous Bayesian inference on unknown or complex chemical and biological reaction systems. \n  \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-10-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:20231117T110000
DTEND;TZID=Asia/Seoul:20231117T120000
DTSTAMP:20260524T104732
CREATED:20230831T143713Z
LAST-MODIFIED:20240728T143844Z
UID:8408-1700218800-1700222400@www.ibs.re.kr
SUMMARY:Samuel Isaacson\, Spatial Particle Modeling of Immune Processes
DESCRIPTION:Abstract: \nSurface Plasmon Resonance (SPR) assays are a standard approach for quantifying kinetic parameters in antibody-antigen binding reactions. Classical SPR approaches ignore the bivalent structure of antibodies\, and use simplified ODE models to estimate effective reaction rates for such interactions. In this work we develop a new SPR protocol\, coupling a model that explicitly accounts for the bivalent nature of such interactions and the limited spatial distance over which such interactions can occur\, to a SPR assay that provides more features in the generated data. Our approach allows the estimation of bivalent binding kinetics and the spatial extent over which antibodies and antigens can interact\, while also providing substantially more robust fits to experimental data compared to classical ODE models. I will present our new modeling and parameter estimation approach\, and demonstrate how it is being used to study interactions between antibodies and spike protein. I will also explain how we make the overall parameter estimation problem computationally feasible via the construction of a surrogate approximation to the (computationally-expensive) particle model. The latter enables fitting of model parameters via standard optimization approaches. \nTime-permitting\, I will also give an introduction to our Catalyst.jl symbolic chemical reaction modeling library\, which we have recently demonstrated outperforms a number of popular systems biology simulation packages in solving ODE and stochastic reaction models. A distinguishing feature of Catalyst is the ease with which it integrates with other Julia libraries to enable sensitivity analysis\, parameter estimation studies\, structural identifiability analysis\, bifurcation analysis\, solution of the chemical master equation\, and a variety of higher-level functionality.
URL:https://www.ibs.re.kr/bimag/event/samuel-isaacson-spatial-particle-modeling-of-immune-processes/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/08/Samuel-Isaacson-scaled-e1722177501809.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231122T160000
DTEND;TZID=Asia/Seoul:20231122T170000
DTSTAMP:20260524T104732
CREATED:20230831T143538Z
LAST-MODIFIED:20240728T143214Z
UID:8405-1700668800-1700672400@www.ibs.re.kr
SUMMARY:Alfio Quarteroni\, Physics-based and data-driven numerical models for computational medicine
DESCRIPTION:Abstract: I will report on some recent results on modelling the heart\, the external circulation\, and their application to problems of clinical relevance. I will show that a proper integration between PDE-based and machine-learning algorithms can improve the computational efficiency and enhance the generality of our iHEART simulator.
URL:https://www.ibs.re.kr/bimag/event/alfio-quarteroni-physics-based-and-data-driven-numerical-models-for-computational-medicine/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/08/Alfio-Quarteroni-e1722177125537.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231124T140000
DTEND;TZID=Asia/Seoul:20231124T160000
DTSTAMP:20260524T104732
CREATED:20231030T040641Z
LAST-MODIFIED:20231114T081738Z
UID:8663-1700834400-1700841600@www.ibs.re.kr
SUMMARY:Dongju Lim\, An accurate probabilistic step finder for time-series analysis
DESCRIPTION:We will discuss about “An accurate probabilistic step finder for time-series analysis.” bioRxiv (2023): 2023-09. \nAbstract \n\n\n\n\nNoisy time-series data is commonly collected from sources including Förster Resonance Energy Transfer experiments\, patch clamp and force spectroscopy setups\, among many others. Two of the most common paradigms for the detection of discrete transitions in such time-series data include: hidden Markov models (HMMs) and step-finding algorithms. HMMs\, including their extensions to infinite state-spaces\, inherently assume in analysis that holding times in discrete states visited are geometrically–or\, loosely speaking in common language\, exponentially–distributed. Thus the determination of step locations\, especially in sparse and noisy data\, is biased by HMMs toward identifying steps resulting in geometric holding times. In contrast\, existing step-finding algorithms\, while free of this restraint\, often rely on ad hoc metrics to penalize steps recovered in time traces (by using various information criteria) and otherwise rely on approximate greedy algorithms to identify putative global optima. Here\, instead\, we devise a robust and general probabilistic (Bayesian) step-finding tool that neither relies on ad hoc metrics to penalize step numbers nor assumes geometric holding times in each state. As the number of steps themselves in a time-series are\, a priori unknown\, we treat these within a Bayesian nonparametric (BNP) paradigm. We find that the method developed\, Bayesian Nonparametric Step (BNP-Step)\, accurately determines the number and location of transitions between discrete states without any assumed kinetic model and learns the emission distribution characteristic of each state. In doing so\, we verify that BNP-Step can analyze sparser data sets containing higher noise and more closely-spaced states than otherwise resolved by current state-of-the-art methods. What is more\, BNP-Step rigorously propagates measurement uncertainty into uncertainty over state transition locations\, numbers\, and emission levels as characterized by the posterior. We demonstrate the performance of BNP-Step on both synthetic data as well as data drawn from force spectroscopy experiments. \n 
URL:https://www.ibs.re.kr/bimag/event/2023-11-24-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
END:VCALENDAR