Biomedical Mathematics Group

Mathematical modeling of infectious disease dynamics - Sang Woo Park
2025-11-12 15:00 - 16:00 (Asia/Seoul) | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract
Recent emergence and re-emergence of infectious disease pathogens have caused major disruptions to our society, highlighting the importance of managing ongoing outbreaks and predicting future epidemics. In this talk, I will use mathematical models to test biological hypotheses about pathogen transmission and leverage these findings to inform public health guidance. I will begin by modeling the transmission dynamics of Enterovirus D68 as a case study. I then use mathematical models from ecological perspective to answer questions about pathogen coexistence, responses to perturbations, and climate drivers. Overall, I will provide a broad overview to highlight the use of mathematical models in answering core questions in infectious disease ecology.

(Cancelled) TBD - Amir Sharafkhaneh
2025-11-12 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
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Generative Models and Causality - Kyungwoo Song
2025-11-12 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
This seminar examines how generative AI advances three foundational tasks in causality, treated as distinct, modular problems: (1) causal inference via intervention‑effect estimation, (2) causal graph analysis, and (3) detection of causal mechanism shifts and change points. First, for causal inference, we consider procedures in which generative models align domain knowledge with observational signals to represent treatment, confounding, and temporal context. This enables stable estimation of intervention effects and principled policy evaluation without relying on explicit counterfactual generation. Second, for causal graph analysis, we outline strategies that combine language‑grounded knowledge extraction and constraint proposals with statistical checks to improve the reliability of directionality and structure, yielding interpretable hypothesis spaces and testable causal claims. Third, for shift detection, we describe methods that disentangle changes in functional mechanisms from changes in noise, supporting early diagnosis of performance degradation, targeting of monitoring resources, and evidence‑based model updates in deployed settings. Across these tasks, generative AI serves as a computational aide for knowledge alignment, hypothesis proposal and pruning, uncertainty annotation, and experiment‑design suggestions. We conclude with a brief outlook on a causal agent that orchestrates data ingestion, hypothesis formation, intervention‑effect estimation, shift monitoring, and policy revision, offering an integrated, yet auditable and modular, workflow for reliability‑centered decision support.

Modeling personalized heart rate response to exercise and environmental factors with wearables data - Dongju Lim
2025-11-21 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Modeling personalized heart rate response to exercise and environmental factors with wearables data” by Nazaret et al., npj digital medicine, 2023.
Abstract
Heart rate (HR) response to workout intensity re ects tness and cardiorespiratory health. Physiological models have been developed to describe such heart rate dynamics and characterize cardiorespiratory tness. However, these models have been limited to small studies in controlled lab environments and are challenging to apply to noisy—but ubiquitous—data from wearables. We propose a hybrid approach that combines a physiological model with exible neural network components to learn a personalized, multidimensional representation of tness. The physiological model describes the evolution of heart rate during exercise using ordinary differential equations (ODEs). ODE parameters are dynamically derived via a neural network connecting personalized representations to external environmental factors, from area topography to weather and instantaneous workout
intensity. Our approach ef ciently ts the hybrid model to a large set of 270,707 workouts collected from wearables of 7465 users from the Apple Heart and Movement Study. The resulting model produces tness representations that accurately predict full HR response to exercise intensity in future workouts, with a per-workout median error of 6.1 BPM [4.4–8.8 IQR]. We further demonstrate that the learned representations correlate with traditional metrics of cardiorespiratory tness, such as VO2 max (explained variance
0.81 ± 0.003). Lastly, we illustrate how our model is naturally interpretable and explicitly describes the effects of environmental factors such as temperature and humidity on heart rate, e.g., high temperatures can increase heart rate by 10%. Combining physiological ODEs with exible neural networks can yield interpretable, robust, and expressive models for health applications.

2025 KAI-X Global Conference in Sleep Synergy
2025-12-01 09:30 - 2025-12-02 15:00 | KAIST W13 Conference Room (1F), 291 Daehak-ro Yuseong-gu, Daejeon, Daejeon, 34141, Korea, Republic of
Conference Webpage Link: https://sites.google.com/view/2025-kai-x-sleep-synergy/home

Empirical modeling of bifurcations and chaos from time series - Stephan Munch
2025-12-05 11:00 - 12:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
Abstract
Many natural systems exhibit complex dynamics and are prone to sudden changes or ‘regime shifts’. At the same time, many of these systems are sparsely observed posing considerable challenges for modeling and control. Here I will describe recent developments in empirical dynamic modeling (EDM) for inference of bifurcations and anticipation of unseen dynamical regimes from ecological time series.

Quantifying interventional causality by knockoff operation - Yun Min Song
2025-12-12 11:00 - 12:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Causal disentanglement for single-cell representations and controllable counterfactual generation” by Yicheng Gao et al., Nature Communications, 2025.
Abstract
Conducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations, with the aim of increasing the explainability, generalizability and controllability of single-cell data, including spatial-temporal omics data, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios, i.e., disentanglement and reconstruction, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting - Jinwoo Hyun
2025-12-26 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “N-BEATS: Neural basis expansion analysis for interpretable time series forecasting” by B. Oreshkin et al., ICLR, 2020.
Abstract
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year’s winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

Distribution shift in machine learning: robustness, invariance, and a causal view - Wooseok Ha
2025-12-29 16:30 - 18:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Classical machine learning models are typically trained under the assumption that the training (source) and test (target) data are drawn from the same distribution. However, real-world data are rarely clean or consistent, and distribution shifts between the source and target domains are ubiquitous. Despite its importance, addressing distribution shifts is highly difficult. The fundamental challenge is that the problem is mathematically ill-posed: shifts can occur in many different forms, and no single method can handle all of them. While numerous algorithms have been proposed in recent years to solve distribution shifts, most are empirical-driven and lack solid foundations. In this talk, I will provide a broad overview of approaches to address distribution shift based on invariance and distributional robustness, and explain how these methods are intrinsically connected to a causal perspective. In particular, I will show why it is crucial to carefully formulate assumptions that relate the source and target domains for reliable generalization, and how assumptions grounded in the causal system enable the analysis of algorithms under both unsupervised and semi-supervised settings.

Expanding the Data Analysis Toolkit: Explainable AI, Causal Learning, and Time-Series Foundation Models - Daeil Jang
2025-12-30 15:00 - 16:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Recent advances in data science have expanded the scope of data analysis beyond prediction accuracy toward interpretability, causal understanding, and generalizable learning across complex data structures. This lecture introduces three emerging methodological approaches that can be directly leveraged in modern data analysis workflows.
First, the lecture presents explainable artificial intelligence (XAI) techniques, focusing on SHAP and its extension to time-series explainability, to illustrate how model predictions can be decomposed into meaningful variable- and time-specific contributions. Second, it introduces machine-learning and deep-learning–based causal inference models, highlighting how these methods move beyond association to estimate intervention effects and heterogeneous impacts while maintaining interpretability. Third, the lecture explores recent time-series foundation models—such as Lag-LLaMA and TabPFN-based approaches—that enable transferable learning across diverse time-series tasks with minimal task-specific training.
Rather than treating these approaches as isolated research trends, this lecture frames them as complementary analytical tools that address key questions in data analysis: What drives model predictions? What would change under intervention? And how can models generalize across time and settings? Through this integrated perspective, the lecture aims to provide practical insight into how these three methods can be applied to real-world data analysis and inspire new research and application opportunities.

Rationalizing Therapeutics: Mathematical Insights into Drug and Cell Therapy Development - Seokjoo Chae
2025-12-30 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Mathematical modeling provides essential quantitative insights that accelerate drug and cell therapy development. In this presentation, we utilize kinetic frameworks to optimize the design of molecular glues by elucidating their biophysical determinants and identify a key target for NK cell-mediated immunotherapy through systematic data analysis. Collectively, we demonstrate how mathematical strategies can effectively guide and advance the development of next-generation therapeutics.

Seasonal timing and interindividual differences in shiftwork adaptation - Kang Min Lee
2026-01-02 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Seasonal timing and interindividual differences in shiftwork adaptation” by R. Kim et al., npj digital medicine, 2025.
Abstract
Millions of shift workers in the U.S. face an increased risk of depression, cancer, and metabolic disease, yet individual responses to shift work vary widely. We find that a conserved biological system of morning and evening oscillators, which evolved for seasonal timing, may contribute to these interindividual differences. In this study, we analyze seasonality in medical interns working shifts, revealing that summer-winter variation correlates with increased circadian misalignment after shift work. Mathematical modeling suggests that seasonal timing influences the rate of adaptation to new schedules, predicting differential effects on morning and evening oscillators. Additionally, we examine genetic polymorphisms linked to seasonality in animals and find that human variants can impact how quickly circadian rhythms respond to schedule changes. Based on our findings, we hypothesize that the vast interindividual differences in shift work adaptation—critical for shift worker health—can in part be explained by biological mechanisms for seasonal timing.

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction - Aqsa Awan
2026-01-09 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al., arxiv, 2025.
Abstract
This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes, unseen covariate combinations (UC) and unseen drugs (UD), scPPDM sets new state-of-the-art results across log fold-change recovery, delta correlations, explained variance, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning, reducing experimental burden while preserving biological specificity.

Leveraging Large-Scale Perturbome Data for Complex Disease Target Discovery- Sang-Min Park
2026-01-14 15:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Complex diseases, such as cancer, sarcopenia, and immune disorders, arise from abnormalities in multiple genes and pathways, posing significant challenges to conventional single-target drug discovery strategies. To address this, we developed a perturbome-based analytical framework that integrates transcriptomic signatures, network pharmacology, and machine learning to identify effective therapeutic candidates. Central to this approach is the KORE-Map (Korean Medicine Omics Resource Extension Map), a systematically curated transcriptomic repository of herbal medicine perturbations in diverse cellular and disease contexts. Using KORE-Map, we reconstructed perturbome landscapes of traditional prescriptions such as Bojungikki-tang and Jakyak-gamcho-tang, as well as natural compounds including ginsenosides and licochalcone B. By integrating differential expression, pathway activity, and synergic index modeling, we demonstrated how perturbome data can reveal druggable axes for overcoming resistance to targeted and immune therapies in cancer, attenuating muscle atrophy, and modulating inflammatory responses. Importantly, perturbome-guided candidate prioritization was validated through multi-omics profiling and functional assays, underscoring its translational value. Our findings highlight perturbome data analysis as a powerful strategy for navigating the complexity of disease biology and accelerating drug discovery.

Quantifying interventional causality by knockoff operation - Olive Cawiding
2026-01-15 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper, “Quantifying interventional causality by knockoff operation” by Xinyan Zhang and Luonan Chen, Science Advances, 2025.
Abstract
Causal inference between measured variables is crucial to understand the underlying mechanism of complex biological processes at a network level but remains challenging in computational biology. We propose an innovative causal criterion, knockoff conditional mutual information (KOCMI), to accurately infer interventional direct causality without prior knowledge of the network structure using either time-independent or time-series data. KOCMI performs knockoff operation on a variable as its virtual intervention, which preserves the original network structure, and then identifies the causality between two variables by estimating the distributional invariance before and after such a virtual intervention. We show that, algorithmically, KOCMI enables quantification of causal relationship, even for networks with loops, and, theoretically, is also consistent with the do-calculus causal analyses but without their prerequisite of the network structure. KOCMI shows superior performance on benchmark and real datasets, comparing with existing methods. Overall, KOCMI provides a powerful tool in inferring interventional causality, which is theoretically ensured and experimentally validated by real intervention data.

A wearable-based aging clock associates with disease and behavior - Myna Lim
2026-01-23 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper, “A wearable-based aging clock associates with disease and behavior” by A. C. Miller et al., Nature Comm, 2025.
Abstract
Aging biomarkers play a vital role in understanding longevity, with the potential to improve clinical decisions and interventions. Existing aging clocks typically use blood, vitals, or imaging collected in a clinical setting. Wearables, in contrast, can make frequent and inexpensive measurements throughout daily living. Here we develop PpgAge, an aging clock using photoplethysmography at the wrist from a consumer wearable. Using the Apple Heart & Movement Study (n = 213,593 participants; >149 million participant-days), our observational analysis shows that this non-invasive and passively collected aging clock accurately predicts chronological age and captures signs of healthy aging. Participants with an elevated PpgAge gap (i.e., predicted age greater than chronological age) have significantly higher diagnosis rates of heart disease, heart failure, and diabetes. Elevated PpgAge gap is also a significant predictor of incident heart disease events (and new diagnoses) when controlling for relevant risk factors. PpgAge also associates with behavior, including smoking, exercise, and sleep. Longitudinally, PpgAge exhibits a sharp increase during pregnancy and concurrent with certain types of cardiac events.

Generic Temperature Response of Large Biochemical Networks - Shingo Gibo
2026-01-30 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Generic Temperature Response of Large Biochemical Networks” by Julian B. Voits and Ulrich S. Schwarz, PRX Life, 2025.
Abstract
Biological systems are remarkably susceptible to relatively small temperature changes. The most obvious example is fever, when a modest rise in body temperature of only few Kelvin has strong effects on our immune system and how it fights pathogens. Another very important example is climate change, when even smaller temperature changes lead to dramatic shifts in ecosystems. Although it is generally accepted that the main effect of an increase in temperature is the acceleration of biochemical reactions according to the Arrhenius equation, it is not clear how it affects large biochemical networks with complicated architectures. For developmental systems such as fly and frog, it has been shown that the system response to temperature deviates in a characteristic manner from the linear Arrhenius plot of single reactions, but a rigorous explanation has not been given yet. Here we use a graph-theoretical interpretation of the mean first-passage times of a biochemical master equation to give a statistical description. We find that in the limit of large system size and if the network has a bias towards a target state, then the Arrhenius plot is generically quadratic, in excellent agreement with numerical simulations for large networks as well as with experimental data for developmental times in fly. We also discuss under which conditions this generic response can be violated, for example for linear chains, which have only one spanning tree.

Multi-Marginal Flow Matching with Adversarially Learnt Interpolants - Gyuyoung Hwang
2026-02-06 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Multi-Marginal Flow Matching with Adversarially Learnt Interpolants” by O. Kviman et al., 2025, arxiv.
Abstract
Learning 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.

IBS BIMAG 2025 Winter Internship Presentation
2026-02-10 13:30 - 15:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Assigned time
Chair
Topic
Mentee
Mentor
Title

13:30–13:35
Myna Lim
Digital Health &
Clinical Methodology
Suhyeon Hwang
Myna Lim
Development of a Shortened Version of Cognitive Flexibility Inventory (CFI)

13:35–13:40
Sugwon Cho
Myna Lim
Machine Learning–Based Development of a Short-Form Scale for Subjective Perceptions of Sleep Medications

13:45–13:50
Taekeun Kim
Kangmin Lee
Revealing pattern of slow wave activity for insomnia diagnosis

13:50–13:55
Jeongmin Kim
Jin Woo Hyun
Iterative Multi-Kernel Self-Supervised Learning for Multimodal Wearable Data

13:55–14:00
Kangmin Lee
Sleep
Minjae Kim
Kangmin Lee
Improving prediction of circadian phase with mathematical model of sleep

14:00–14:05
Daewon Jeong
Yun Min Song
Analysis of Sleep Patterns Leading to Improved Sleep and Enhanced Alertness

14:05–14:10
Seunghun Lee
Yun Min Song
Analysis on relation between alertness and sleep quality during treatment

14:10–14:15
Hyeong Jun Jang
Molecular &
Cellular dynamics
Junyoung Lee
Hyeong Jun Jang
AI-based expansion of the validity condition for enzyme kinetic model

14:15–14:20
Se Jun Ahn
Hyeong Jun Jang
Practical application of 2D-3D reaction-diffusion model in transporter

14:20–14:25
Jaehun Jeong
Gyuyoung Hwang
Unifying framework for circadian temperature robustness: The roles of waveform and intercellular coupling

14:25–14:30
Jaehyuk Yang
Hyun Kim
Improving Spatial Gene Prediction via scLENS-Driven SpaGE

14:30–14:35
Math & AI
for dynamics
Gia Hyun
Dongju Lim
Weak form estimation of history-dependent epidemiological dynamics

14:35–14:40
Gyeongwan Gu
Jin Woo Hyun
Deep Predictor-Corrector Networks for Robust Parameter Estimation in Non-autonomous System with Discontinuous Inputs

14:40–14:45
Jiwon Jang
Gyuyoung Hwang
Flow matching with Physics-informed principle to solve inverse problem

14:45–14:50
Minjun Kim & Taekwan Kim
Kangmin Lee
Characterizing Circadian Modulation of Heart Rate Through Physics-Informed Neural Network Inference

Toward a Foundation Model for Molecular Tasks - Sungbin Lim
2026-02-10 16:00 - 18:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract
(국문) 최근 거대언어모델(LLM)을 기술의 발전은 AI4Science 분야에서 Foundation Model 개발에 대한 세계적인 관심을 촉발하였다. 그 중에서도 신약 및 신소재 개발에 연계된 Molecular 도메인에서의 Foundation Model 연구는 막대한 산업적 영향력과 가치를 가지고 있다. 본 발표에서는 분자 구조 생성, 물성, 및 반응 예측 문제에 적용되기 위해 필요한 Multimodal LLM 연구 성과와 방향성을 소개하고자 한다.
(English) The advancement of Large Language Models (LLMs) is drawing huge interest in developing Foundation Models for the field of AI4Science. In particular, Foundation Model research within the molecular domain, specifically linked to drug discovery and advanced materials, holds immense industrial impact and value. In this presentation, we introduce recent achievements in Multimodal LLM research for molecular structure generation, property prediction, and chemical reaction forecasting. This is a joint work with LG AI Research.

Intelligent in-cell electrophysiology - Chitaranjan Mahapatra
2026-02-13 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
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.
Abstract
Intracellular 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.

GenAI-Net: A Generative AI Framework for Automated Biomolecular Network Design - Dongju Lim
2026-02-20 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
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.
Abstract
Biomolecular 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.

TwinCell: Large Causal Cell Model for Reliable and Interpretable Therapeutic Target Prioritisation - Yun Min Song
2026-02-27 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
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.
Abstract
Drug 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.

A multimodal sleep foundation model for disease prediction - Jinwoo Hyun
2026-03-13 10:00 - 23:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “A multimodal sleep foundation model for disease prediction” by Rahul Thapa et al., Nature Medicine, 2026.
Abstract
Sleep is a fundamental biological process with broad implications for physical and mental health, yet its complex relationship with disease remains poorly understood. Polysomnography (PSG)—the gold standard for sleep analysis—captures rich physiological signals but is underutilized due to challenges in standardization, generalizability and multimodal integration. To address these challenges, we developed SleepFM, a multimodal sleep foundation model trained with a new contrastive learning approach that accommodates multiple PSG configurations. Trained on a curated dataset of over 585,000 hours of PSG recordings from approximately 65,000 participants across several cohorts, SleepFM produces latent sleep representations that capture the physiological and temporal structure of sleep and enable accurate prediction of future disease risk. From one night of sleep, SleepFM accurately predicts 130 conditions with a C-Index of at least 0.75 (Bonferroni-corrected P < 0.01), including all-cause mortality (C-Index, 0.84), dementia (0.85), myocardial infarction (0.81), heart failure (0.80), chronic kidney disease (0.79), stroke (0.78) and atrial fibrillation (0.78). Moreover, the model demonstrates strong transfer learning performance on a dataset from the Sleep Heart Health Study—a dataset that was excluded from pretraining—and performs competitively with specialized sleep-staging models such as U-Sleep and YASA on common sleep analysis tasks, achieving mean F1 scores of 0.70–0.78 for sleep staging and accuracies of 0.69 and 0.87 for classifying sleep apnea severity and presence. This work shows that foundation models can learn the language of sleep from multimodal sleep recordings, enabling scalable, label-efficient analysis and disease prediction.

Temporal tissue dynamics from a spatial snapshot - Kang Min Lee
2026-03-20 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “Temporal tissue dynamics from a spatial snapshot” by Jonathan Somer et al., Nature, 2026.
Abstract
Physiological and pathological processes such as inflammation and cancer emerge from interactions between cells over time1. However, methods to follow cell populations over time within the native context of a human tissue are lacking because a biopsy offers only a single snapshot. Here we present one-shot tissue dynamics reconstruction (OSDR), an approach to estimate a dynamical model of cell populations based on a single tissue sample. OSDR uses spatial proteomics to learn how the composition of cellular neighbourhoods influences division rate, providing a dynamical model of cell population change over time. We apply OSDR to human breast cancer data2,3,4, and reconstruct two fixed points of fibroblasts and macrophage interactions5,6. These fixed points correspond to hot and cold fibrosis7, in agreement with co-culture experiments that measured these dynamics directly8. We then use OSDR to discover a pulse-generating excitable circuit of T and B cells in the tumour microenvironment, suggesting temporal flares of anticancer immune responses. Finally, we study longitudinal biopsies from a triple-negative breast cancer clinical trial3, in which OSDR predicts the collapse of the tumour cell population in responders but not in non-responders, based on early-treatment biopsies. OSDR can be applied to a wide range of spatial proteomics assays to enable analysis of tissue dynamics based on patient biopsies.

Stochastic theory of complex biochemical reaction networks - Chen Jia
2026-03-25 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Biochemical reaction networks and gene regulatory networks in cells are prototypical examples of complex systems, characterized by highly nonlinear and stochastic, multilevel dynamical interactions. Gaining a deep understanding of the stochastic dynamics and thermodynamic principles governing biochemical reaction networks not only helps elucidate the intrinsic mechanisms underlying cell fate decisions and the onset and progression of diseases, but also provides new theoretical paradigms for the study of complex systems. This line of research has become one of the forefront interdisciplinary areas, bridging mathematics, physics, biology, chemistry, statistics, and intelligent science. In this talk, I will present our recent research progress in this area, with the hope of stimulating further discussion and inspiring new ideas.

Zoom : 997 8258 4700 (pw : [REDACTED]

A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games - Fanpeng Song
2026-03-27 10:00 - 11:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this talk, we discuss the paper “A multi-agent reinforcement learning framework for exploring dominant strategies in iterated and evolutionary games” by Qi Su et al., Nat. Comm., 2026.
Abstract
Exploring dominant strategies in iterated games holds theoretical and practical significance across diverse domains. Previous studies, through mathematical analysis of limited cases, have unveiled classic strategies such as tit-for-tat, generous-tit-for-tat, win-stay-lose-shift, and zero-determinant strategies. While these strategies offer valuable insights into human decision-making, they represent only a small subset of possible strategies, constrained by limited mathematical and computational tools available to explore larger strategy spaces. To bridge this gap, we propose an approach using multi-agent reinforcement learning to delve into complex decision-making processes that go beyond human intuition. Our approach has led to the discovery of a strategy that we call memory-two bilateral reciprocity strategy. Memory-two bilateral reciprocity strategy consistently outperforms a wide range of strategies in pairwise interactions while achieving high payoffs. When introduced into an evolving population with diverse strategies, memory-two bilateral reciprocity strategy demonstrates dominance and fosters higher levels of cooperation and social welfare in both homogeneous and heterogeneous structures, as well as across various game types. This high performance is verified by simulations and mathematical analysis. Our work highlights the potential of multi-agent reinforcement learning in uncovering dominant strategies in iterated and evolutionary games.

Stochastics in medicine: Delaying menopause and missing drug doses - Sean Lawley
2026-04-03 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Stochastic modeling and analysis can help answer pressing medical questions. In this talk, I will attempt to justify this claim by describing recent work on two problems in medicine. The first problem concerns ovarian tissue cryopreservation, which is a proven tool to preserve ovarian follicles prior to gonadotoxic treatments. Can this procedure be applied to healthy women to delay or eliminate menopause? How can it be optimized? The second problem concerns medication nonadherence. What should you do if you miss a dose of medication? How can physicians design dosing regimens that are robust to missed/late doses? I will describe (a) how stochastics theory offers insights into these questions and (b) the mathematical questions that emerge from this investigation.

Zoom : 997 8258 4700 (pw : [REDACTED]

A Data-Driven Computational Framework for Identifiability and Nonlinear Dynamics Discovery in Complex Systems - Wenrui Hao
2026-04-10 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Data-driven modeling is essential for deciphering complex biological systems, yet its utility is often constrained by two fundamental hurdles: the inability to guarantee parameter identifiability and the high computational cost of learning nonlinear dynamics. This talk introduces a unified computational framework designed to overcome these challenges, bridging theoretical rigor with scalable machine learning.

The first component of the framework establishes a computational foundation for practical identifiability. By leveraging the Fisher Information Matrix and its theoretical links to coordinate identifiability, we propose an efficient method for identifiability assessment. We further introduce regularization-based strategies to manage non-identifiable parameters, thereby enhancing model reliability and facilitating robust uncertainty quantification.

To address the discovery of nonlinear dynamics, we present the Laplacian Eigenfunction-Based Neural Operator (LE-NO). This operator learning framework is specifically engineered for modeling reaction–diffusion equations. By projecting nonlinear operators onto Laplacian eigenfunctions, LE-NO achieves superior computational efficiency and generalization across varying boundary conditions, effectively bypassing the limitations of large-scale architectures and data scarcity.

Finally, we demonstrate the framework’s utility in the context of Alzheimer’s disease modeling. We show that this integrated approach ensures reliable parameter inference while capturing the intricate nonlinear dynamics of disease progression, providing a critical step toward the development of high-fidelity digital twins for neurodegenerative pathology.

Zoom : 997 8258 4700 (pw : [REDACTED]

Discovering network dynamics with neural symbolic regression - Olive Cawiding
2026-04-17 10:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
In this tallk, we discuss the paper “Discovering network dynamics with neural symbolic regression” by Zihan Yu et al., Nature Com. Science, 2026.
Abstract
Network dynamics are fundamental to analyzing the properties of high-dimensional complex systems and understanding their behavior. Despite the accumulation of observational data across many domains, mathematical models exist in only a few areas with clear underlying principles. Here we show that a neural symbolic regression approach can bridge this gap by automatically deriving formulas from data. Our method reduces searches on high-dimensional networks to equivalent one-dimensional systems and uses pretrained neural networks to guide accurate formula discovery. Applied to ten benchmark systems, it recovers the correct forms and parameters of underlying dynamics. In two empirical natural systems, it corrects existing models of gene regulation and microbial communities, reducing prediction error by 59.98% and 55.94%, respectively. In epidemic transmission across human mobility networks of various scales, it discovers dynamics that exhibit the same power-law distribution of node correlations across scales and reveal country-level differences in intervention effects. These results demonstrate that machine-driven discovery of network dynamics can enhance understandings of complex systems and advance the development of complexity science.
