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PRODID:-//Biomedical Mathematics Group - ECPv6.16.2//NONSGML v1.0//EN
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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:20220101T000000
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END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231101T160000
DTEND;TZID=Asia/Seoul:20231101T170000
DTSTAMP:20260528T191721
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:20260528T191721
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:20231117T110000
DTEND;TZID=Asia/Seoul:20231117T120000
DTSTAMP:20260528T191721
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:20260528T191721
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
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