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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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TZOFFSETFROM:+0900
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260206T100000
DTEND;TZID=Asia/Seoul:20260206T113000
DTSTAMP:20260422T122428
CREATED:20260203T013529Z
LAST-MODIFIED:20260203T013529Z
UID:12164-1770372000-1770377400@www.ibs.re.kr
SUMMARY:Multi-Marginal Flow Matching with Adversarially Learnt Interpolants - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Multi-Marginal Flow Matching with Adversarially Learnt Interpolants” by O. Kviman et al.\, 2025\, arxiv. \nAbstract \nLearning the dynamics of a process given sampled observations at several time points is an important but difficult task in many scientific applications. When no ground-truth trajectories are available\, but one has only snapshots of data taken at discrete time steps\, the problem of modelling the dynamics\, and thus inferring the underlying trajectories\, can be solved by multi-marginal generalisations of flow matching algorithms. This paper proposes a novel flow matching method that overcomes the limitations of existing multi-marginal trajectory inference algorithms. Our proposed method\, ALI-CFM\, uses a GAN-inspired adversarial loss to fit neurally parametrised interpolant curves between source and target points such that the marginal distributions at intermediate time points are close to the observed distributions. The resulting interpolants are smooth trajectories that\, as we show\, are unique under mild assumptions. These interpolants are subsequently marginalised by a flow matching algorithm\, yielding a trained vector field for the underlying dynamics. We showcase the versatility and scalability of our method by outperforming the existing baselines on spatial transcriptomics and cell tracking datasets\, while performing on par with them on single-cell trajectory prediction.
URL:https://www.ibs.re.kr/bimag/event/multi-marginal-flow-matching-with-adversarially-learnt-interpolants-gyuyoung-hwang/
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:20260213T100000
DTEND;TZID=Asia/Seoul:20260213T113000
DTSTAMP:20260422T122428
CREATED:20260203T013928Z
LAST-MODIFIED:20260203T013928Z
UID:12166-1770976800-1770982200@www.ibs.re.kr
SUMMARY:Intelligent in-cell electrophysiology - Chitaranjan Mahapatra
DESCRIPTION:In this talk\, we discuss the paper “Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings” by K. Rahmani et al.\, Nat. Comm\, 2025. \nAbstract \nIntracellular electrophysiology is essential in neuroscience\, cardiology\, and pharmacology for studying cells’ electrical properties. Traditional methods like patch-clamp are precise but low-throughput and invasive. Nanoelectrode Arrays (NEAs) offer a promising alternative by enabling simultaneous intracellular and extracellular action potential (iAP and eAP) recordings with high throughput. However\, accessing intracellular potentials with NEAs remains challenging. This study presents an AI-supported technique that leverages thousands of synchronous eAP and iAP pairs from stem-cell-derived cardiomyocytes on NEAs. Our analysis revealed strong correlations between specific eAP and iAP features\, such as amplitude and spiking velocity\, indicating that extracellular signals could be reliable indicators of intracellular activity. We developed a physics-informed deep learning model to reconstruct iAP waveforms from extracellular recordings recorded from NEAs and Microelectrode arrays (MEAs)\, demonstrating its potential for non-invasive\, long-term\, high-throughput drug cardiotoxicity assessments. This AI-based model paves the way for future electrophysiology research across various cell types and drug interactions.
URL:https://www.ibs.re.kr/bimag/event/intelligent-in-cell-electrophysiology-chitaranjan-mahapatra/
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:20260220T100000
DTEND;TZID=Asia/Seoul:20260220T113000
DTSTAMP:20260422T122428
CREATED:20260203T014207Z
LAST-MODIFIED:20260219T012056Z
UID:12168-1771581600-1771587000@www.ibs.re.kr
SUMMARY:GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design - Dongju Lim
DESCRIPTION:In this talk\, we discuss the paper “GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design” by M. Filo et al.\, arxiv\, 2026. \nAbstract \nBiomolecular networks underpin emerging technologies in synthetic biology—from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics—and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation\, the reverse problem of discovering a network from a behavioral specification is difficult\, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net\, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel\, topologically diverse solutions across multiple design tasks\, in- cluding dose responses\, complex logic gates\, classifiers\, oscillators\, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs\, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.
URL:https://www.ibs.re.kr/bimag/event/quantum-inspired-approach-to-analyzing-complex-system-dynamics-dongju-lim/
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:20260227T100000
DTEND;TZID=Asia/Seoul:20260227T113000
DTSTAMP:20260422T122428
CREATED:20260219T012250Z
LAST-MODIFIED:20260219T012250Z
UID:12219-1772186400-1772191800@www.ibs.re.kr
SUMMARY:TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation” by J.-B. Morlot et al.\, bioarxiv\, 2026. \nAbstract \nDrug discovery is impeded by the difficulty of translating targets from preclinical models to patients. Here\, we present TwinCell\, a Large Causal Cell Model (LCCM) capable of generalising from in vitro cell lines to patient-derived cell types while providing biologically meaningful interpretations of its predictions. Rather than predicting perturbation outcomes\, TwinCell focuses on identifying the upstream regulators driving the transition between diseased and healthy states. By integrating single-cell foundation model embeddings with a multiomics interactome\, TwinCell constrains predictions to mechanistically plausible signalling routes. To validate this approach\, we introduce TwinBench\, a novel benchmarking framework to assess virtual cell models generalisation capability while correcting for popularity bias and mode collapse through empirical p-value estimation. When applied to in clinico data\, TwinCell recovers known drug targets aligned with the biology of the disease and demonstrates generalisation performance on targets unseen during training. This highlights the model’s ability to learn mechanistic principles in a biological context. TwinCell represents a significant step toward building reliable and interpretable virtual cells for target identification bridging the gap between high-throughput in vitro experiments and clinical insights.
URL:https://www.ibs.re.kr/bimag/event/twincell-large-causal-cell-model-for-reliable-and-interpretable-therapeutic-target-prioritisation-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
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