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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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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260703T100000
DTEND;TZID=Asia/Seoul:20260703T120000
DTSTAMP:20260528T012333Z
CREATED:20260527T140141Z
LAST-MODIFIED:20260528T012333Z
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SUMMARY:A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations - Se Jun Ahn
DESCRIPTION:In this talk\, we discuss the paper “A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations” by Harkirat Sigh Arora et al.\, npj drug discovery\, 2026. \nAbstract: \nAntimicrobial resistance poses a major global threat\, driven by diminishing efficacy of current treatments and limited new therapies. Combination therapy with existing drugs offers a promising solution\, yet current empirical screening methods are expensive and often lead to suboptimal efficacy and inadvertent toxicity. We introduce CALMA\, a computational framework that quantitatively analyzes the potency-toxicity landscape of multi-drug combinations. Integrating genome-scale metabolic modeling with a neural network that reflects metabolic subsystems\, CALMA enhances interpretability and prioritizes pathways influencing drug interactions. The incorporation of metabolic architecture in the neural network leads to over 92% reduction in model parameters\, enabling it to learn generalizable mechanistic signals and reducing the experimental search space of optimal combinations by 97%. CALMA identified promising antimicrobial combinations against Escherichia coli and Mycobacterium tuberculosis that were antagonistic for kidney and liver toxicity and uncovered the nucleotide salvage pathway as a selective influencer of toxicity\, which was validated in vitro. Mining of health records of over 400\,000 patients showed reduced frequency of kidney side-effects in patients taking a vancomycin combination identified by CALMA. CALMA provides a rational\, mechanistic approach to streamline combination treatment design.
URL:https://www.ibs.re.kr/bimag/event/a-metabolism-informed-neural-network-identifies-pathways-influencing-the-potency-and-toxicity-of-antimicrobial-combinations-se-jun-ahn/
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
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260710T100000
DTEND;TZID=Asia/Seoul:20260710T120000
DTSTAMP:20260629T080759Z
CREATED:20260629T080759Z
LAST-MODIFIED:20260629T080759Z
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SUMMARY:Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA” by Zhuohan Yu et al.\, nature communications\, 2023. \nAbstract: \n\n\n\nSingle-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality\, and the prevalence of dropout events. To address these concerns\, we develop a deep graph learning method\, scMGCA\, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction\, outperforming other state-of-the-art models across multiple platforms. In addition\, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset\, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.
URL:https://www.ibs.re.kr/bimag/event/topological-identification-and-interpretation-for-single-cell-gene-regulation-elucidation-across-multiple-platforms-using-scmgca-yun-min-song/
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:20260724T100000
DTEND;TZID=Asia/Seoul:20260724T120000
DTSTAMP:20260713T062127Z
CREATED:20260713T062127Z
LAST-MODIFIED:20260713T062127Z
UID:12700-1784887200-1784894400@www.ibs.re.kr
SUMMARY:Topology identifies concurrent cyclic processes in single-cell transcriptomics and androgen receptor function - Seongjin Choi
DESCRIPTION:In this talk\, we discuss the paper “Topology identifies concurrent cyclic processes in single-cell transcriptomics and androgen receptor function” by Kelly Maggs et al.\, bioRxiv\, 2025. \nAbstract: \n\nStandard single-cell RNA-seq analysis frameworks aggregate over-lapping biological processes and impose a single parametrization\, conflating distinct programs. Here\, we introduce a topological framework that detects and disentangles multiple cyclic processes directly from single-cell transcriptomic data. We validate this approach on synthetic datasets and scRNA-seq profiles of human dermal fibroblasts under control conditions and following androgen receptor (AR) silencing\, as well as in vivo mouse prostate regeneration under androgen receptor add-back. We show robust cell cycle structure across conditions\, identify an unbiased AR-linked stress signature related to the senescence and proliferation across organisms\, and uncover cholesterol homeostasis as an AR-linked program in tissue regeneration. This framework enables identification and separation of concurrent cyclic processes from snapshot single-cell data\, revealing complex multi-dimensional regulatory dynamics inaccessible to standard clustering analysis.
URL:https://www.ibs.re.kr/bimag/event/topology-identifies-concurrent-cyclic-processes-in-single-cell-transcriptomics-and-androgen-receptor-function-seongjin-choi/
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:20260731T100000
DTEND;TZID=Asia/Seoul:20260731T120000
DTSTAMP:20260730T020723Z
CREATED:20260629T081120Z
LAST-MODIFIED:20260730T020723Z
UID:12661-1785492000-1785499200@www.ibs.re.kr
SUMMARY:Fast training of accurate physics-informed neural networks without gradient descent - Seunghun Lee
DESCRIPTION:In this talk\, we discuss the paper “Fast training of accurate physics-informed neural networks without gradient descent” by Chinmay Datar et al.\, ICLR\, 2026. \nAbstract: \nSolving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions\, their accuracy and training speed are limited by two core barriers: gradient-descent-based iterative optimization over complex loss landscapes and non-causal treatment of time as an extra spatial dimension. We present Frozen-PINN\, a novel PINN based on the principle of space-time separation that leverages random features instead of training with gradient descent\, and incorporates temporal causality by construction. On nine PDE benchmarks\, including challenges like extreme advection speeds\, shocks\, and high-dimensionality\, Frozen-PINNs achieve superior training efficiency and accuracy over state-of-the-art PINNs\, often by several orders of magnitude. Our work addresses longstanding training and accuracy bottlenecks of PINNs\, delivering quickly trainable\, highly accurate\, and inherently causal PDE solvers\, a combination that prior methods could not realize. Our approach challenges the reliance of PINNs on stochastic gradient-descent-based methods and specialized hardware\, leading to a paradigm shift in PINN training and providing a challenging benchmark for the community.
URL:https://www.ibs.re.kr/bimag/event/oscillatory-network-efficiency-predicts-mood-and-fatigue-during-sleep-deprivation-seunghun-lee/
LOCATION:108\, Conference Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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