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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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DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T110000
DTEND;TZID=Asia/Seoul:20240503T120000
DTSTAMP:20260522T122514
CREATED:20240219T043810Z
LAST-MODIFIED:20240728T142252Z
UID:9239-1714734000-1714737600@www.ibs.re.kr
SUMMARY:Pedro Mendes\, Multiscale hybrid differential equation and agent-based models
DESCRIPTION:Abstract: Biological phenomena are notorious for crossing several temporal and spatial scales. While often it may be sufficient to focus on a single scale\, it is not rare that we have to consider several scales simultaneously. Computational modeling and simulation of biological systems thus frequently requires to include diverse temporal and spatial scales. A popular approach in systems biology is to combine differential equations and agent-based models\, where usually small sets of differential equations are used to represent the internal state of each cell\, with the cells being represented as interacting autonomous agents on a lattice. This type of hybrid models allows for parallel solution of smaller sets of differential equations rather than the solution of a single but very large set of differential equations. At certain discrete times\, the agents are allowed to communicate\, and only then are the different sets of differential equations able to influence each other. This time discretization of the cell-cell interactions carries an inherent approximation error compared to the continuous interaction of these cells in the single model of a large set of coupled differential equations. Here we study this approximation error and investigate the conditions in which it becomes negligible\, thus defining the domain where the multiscale approach is valid. The approach is illustrated with a classic model of Drosophila segment polarity network\, where a model based on a full set of differential equations (the original version of that model) is compared with a hybrid model combining differential equations and agent-based approach (implemented with the open source software simulators Vivarium and COPASI). This study is also relevant to other hybrid simulations\, such as those representing “whole-cell models”\, where partitions may be done at other organizational scales.
URL:https://www.ibs.re.kr/bimag/event/pedro-mendes-multiscale-hybrid-differential-equation-and-agent-based-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/2024/02/Pedro-Mendes-e1722176551946.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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DTSTART;TZID=Asia/Seoul:20240503T150000
DTEND;TZID=Asia/Seoul:20240503T160000
DTSTAMP:20260522T122514
CREATED:20240429T083052Z
LAST-MODIFIED:20240502T050439Z
UID:9543-1714748400-1714752000@www.ibs.re.kr
SUMMARY:(Cancelled) Sung Woong Cho - Estimating the distribution of parameters in differential equations with repeated cross-sectional data
DESCRIPTION:This presentation introduces an approach for estimating parameter distributions in dynamic systems modeled by differential equations. Traditional parameter estimation techniques often struggle with Repeated Cross-Sectional (RCS) data\, characteristic of many real-world scenarios where continuous data collection is impractical or impossible. Previous approaches\, like employing mean values or leveraging Gaussian Processes for time series generation\, fail to capture system parameters’ true heterogeneity and distributions. We introduce a novel approach to infer accurate parameter distributions from RCS data. By constructing artificial trajectories from randomly selected observations at each time point and iteratively refining parameter estimates to minimize discrepancies between observed and modeled dynamics\, our method enables the derivation of true parameter distributions even for RCS data. We demonstrate the efficacy of our method through its application to models including exponential growth\, logistic population dynamics\, and target cell-limited models with delayed virus production. Our findings offer a robust framework for understanding the full complexity of dynamic systems\, paving the way for more precise and insightful analyses across various fields of study.
URL:https://www.ibs.re.kr/bimag/event/sung-woong-cho-estimating-the-distribution-of-parameters-in-differential-equations-with-repeated-cross-sectional-data/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Seminar
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