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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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BEGIN:VTIMEZONE
TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T110000
DTEND;TZID=Asia/Seoul:20240503T120000
DTSTAMP:20260522T110008
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240503T150000
DTEND;TZID=Asia/Seoul:20240503T160000
DTSTAMP:20260522T110008
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240510T110000
DTEND;TZID=Asia/Seoul:20240510T120000
DTSTAMP:20260522T110008
CREATED:20240219T044117Z
LAST-MODIFIED:20240728T142006Z
UID:9242-1715338800-1715342400@www.ibs.re.kr
SUMMARY:Jingyi Jessica Li\, ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double dipping
DESCRIPTION:Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis\, a clustering algorithm is applied to find discrete cell clusters as putative cell types\, and then a statistical test is employed to identify the differentially expressed (DE) genes between the cell clusters. However\, this common procedure suffers the “double dipping” issue: the same data are used twice to find discrete cell clusters as putative cell types and DE genes as potential cell-type marker genes\, leading to false-positive cell-type marker genes even when the cell clusters are spurious. To overcome this challenge\, we propose ClusterDE\, a post-clustering DE method for controlling the false discovery rate (FDR) of identified DE genes regardless of clustering quality\, which can work as an add-on to popular pipelines such as Seurat. The core idea of ClusterDE is to generate real-data-based synthetic null data containing only one cell type\, in contrast to the real data\, for evaluating the whole procedure of clustering followed by a DE test. Using comprehensive simulation and real data analysis\, we show that ClusterDE has solid FDR control and the ability to identify canonical cell-type marker genes as top DE genes\, distinguishing them from common housekeeping genes. Notably\, the DE genes identified by ClusterDE are informative markers for discrete cell types and can guide the merging of spurious clusters. ClusterDE is fast\, transparent\, and adaptive to a wide range of clustering algorithms and DE tests.
URL:https://www.ibs.re.kr/bimag/event/jingyi-jessica-li-clusterde-a-post-clustering-differential-expression-de-method-robust-to-false-positive-inflation-caused-by-double-dipping/
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/Jessica-li-e1722176393718.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240517T140000
DTEND;TZID=Asia/Seoul:20240517T160000
DTSTAMP:20260522T110008
CREATED:20240428T180844Z
LAST-MODIFIED:20240513T082339Z
UID:9532-1715954400-1715961600@www.ibs.re.kr
SUMMARY:Gyuyoung Hwang\, Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming
DESCRIPTION:We will discuss about “Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming”\, Cell (2019). \n  \nAbstract \nUnderstanding the molecular programs that guide differentiation during development is a major challenge. Here\, we introduce Waddington-OT\, an approach for studying developmental time courses to infer ancestor-descendant fates and model the regulatory programs that underlie them. We apply the method to reconstruct the landscape of reprogramming from 315\,000 single-cell RNA sequencing (scRNA-seq) profiles\, collected at half-day intervals across 18 days. The results reveal a wider range of developmental programs than previously characterized. Cells gradually adopt either a terminal stromal state or a mesenchymal-to-epithelial transition state. The latter gives rise to populations related to pluripotent\, extra-embryonic\, and neural cells\, with each harboring multiple finer subpopulations. The analysis predicts transcription factors and paracrine signals that affect fates and experiments validate that the TF Obox6 and the cytokine GDF9 enhance reprogramming efficiency. Our approach sheds light on the process and outcome of reprogramming and provides a framework applicable to diverse temporal processes in biology.
URL:https://www.ibs.re.kr/bimag/event/2024-05-17-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 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:20240524T140000
DTEND;TZID=Asia/Seoul:20240524T160000
DTSTAMP:20260522T110008
CREATED:20240428T181352Z
LAST-MODIFIED:20240428T181352Z
UID:9535-1716559200-1716566400@www.ibs.re.kr
SUMMARY:Kévin SPINICCI\, PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer
DESCRIPTION:We will discuss about “PenDA\, a rank-based method for personalized differential analysis: Application to lung cancer” Plos Computational Biology (2020). \nAbstract \n\nThe hopes of precision medicine rely on our capacity to measure various high-throughput genomic information of a patient and to integrate them for personalized diagnosis and adapted treatment. Reaching these ambitious objectives will require the development of efficient tools for the detection of molecular defects at the individual level. Here\, we propose a novel method\, PenDA\, to perform Personalized Differential Analysis at the scale of a single sample. PenDA is based on the local ordering of gene expressions within individual cases and infers the deregulation status of genes in a sample of interest compared to a reference dataset. Based on realistic simulations of RNA-seq data of tumors\, we showed that PenDA outcompetes existing approaches with very high specificity and sensitivity and is robust to normalization effects. Applying the method to lung cancer cohorts\, we observed that deregulated genes in tumors exhibit a cancer-type-specific commitment towards up- or down-regulation. Based on the individual information of deregulation given by PenDA\, we were able to define two new molecular histologies for lung adenocarcinoma cancers strongly correlated to survival. In particular\, we identified 37 biomarkers whose up-regulation lead to bad prognosis and that we validated on two independent cohorts. PenDA provides a robust\, generic tool to extract personalized deregulation patterns that can then be used for the discovery of therapeutic targets and for personalized diagnosis. An open-access\, user-friendly R package is available at https://github.com/bcm-uga/penda.
URL:https://www.ibs.re.kr/bimag/event/2024-05-24-jc/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240527T160000
DTEND;TZID=Asia/Seoul:20240527T170000
DTSTAMP:20260522T110008
CREATED:20240326T150018Z
LAST-MODIFIED:20240326T150018Z
UID:9426-1716825600-1716829200@www.ibs.re.kr
SUMMARY:Timothy L. Downing\, Biophysical Regulation of Cell Fate\, from ECM to Nuclear Chromatin
DESCRIPTION:Abstract: The Downing lab investigates the intricate biophysical interactions between cells and their environment\, elucidating their role in modulating adult cell behavior and phenotypic transitions via epigenetic regulation of gene expression. Leveraging diverse genome-scale sequencing techniques\, we decipher mechanisms underlying cell fate transitions mediated through dynamic regulation of nuclear chromatin and heterogeneous gene activity. Our research endeavors aim to engineer molecular tools and biomaterials to synthetically modulate the epigenome\, enhancing control over cell fate and behavior. In this seminar presentation\, I will focus on how signaling pathways governing cell-cell and cell-ECM communication contribute to observed fate transitions during the acquisition of stemness phenotypes and lineage plasticity\, particularly in iPSC reprogramming and cancer contexts.
URL:https://www.ibs.re.kr/bimag/event/timothy-l-downing-biophysical-regulation-of-cell-fate-from-ecm-to-nuclear-chromatin/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20240531T140000
DTEND;TZID=Asia/Seoul:20240531T160000
DTSTAMP:20260522T110008
CREATED:20240428T181746Z
LAST-MODIFIED:20240528T001427Z
UID:9538-1717164000-1717171200@www.ibs.re.kr
SUMMARY:Lucas MacQuarrie\, Data driven governing equations approximation using deep neural networks
DESCRIPTION:We will discuss about “Data driven governing equations approximation using deep neural networks” Journal of Computational Physics (2019). \nAbstract \n\nWe present a numerical framework for approximating unknown governing equations using observation data and deep neural networks (DNN). In particular\, we propose to use residual network (ResNet) as the basic building block for equation approximation. We demonstrate that the ResNet block can be considered as a one-step method that is exact in temporal integration. We then present two multi-step methods\, recurrent ResNet (RT-ResNet) method and recursive ReNet (RS-ResNet) method. The RT-ResNet is a multi-step method on uniform time steps\, whereas the RS-ResNet is an adaptive multi-step method using variable time steps. All three methods presented here are based on integral form of the underlying dynamical system. As a result\, they do not require time derivative data for equation recovery and can cope with relatively coarsely distributed trajectory data. Several numerical examples are presented to demonstrate the performance of the methods.
URL:https://www.ibs.re.kr/bimag/event/2024-05-31-jc/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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