Biomedical Mathematics Group

scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction - Aqsa Awan
2026-01-09 10:00 - 11:30 (Asia/Seoul) | 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.

Foundation Models for Wearable Movement Data in Mental Health Research - Aqsa Awan
2026-04-24 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 “Foundation Models for Wearable Movement Data in Mental Health Research” by Franklin Y. Ruan et al., arXiv, 2025.
Abstract
Pretrained foundation models and transformer architectures have driven the success of large language models (LLMs) and other modern AI breakthroughs. However, similar advancements in health data modeling remain limited due to the need for innovative adaptations. Wearable movement data offers a valuable avenue for exploration, as it’s a core feature in nearly all commercial smartwatches, well established in clinical and mental health research, and the sequential nature of the data shares similarities to language. We introduce the Pretrained Actigraphy Transformer (PAT), the first open source foundation model designed for time-series wearable movement data. Leveraging transformer-based architectures and novel techniques, such as patch embeddings, and pretraining on data from 29,307 participants in a national U.S. sample, PAT achieves state-of-the-art performance in several mental health prediction tasks. PAT is also lightweight and easily interpretable, making it a robust tool for mental health research.

Data-driven discovery of biological oscillator models - Lendert Gelens
2026-05-06 16:00 - 17:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Oscillatory dynamics are a found everywhere in living systems, underlying processes such as metabolic regulation, cell division, and embryonic development. Identifying the mechanisms that generate these rhythms is challenging due to nonlinear interactions, multiple time scales, and limited access to all relevant variables. Data-driven approaches offer a promising route to infer dynamical models directly from time-series data. In this talk, I will discuss our work on data-driven discovery of models for (bio)chemical oscillators. In particular, I will present CLINE, a neural-network–based framework that infers key geometric features of phase space, such as nullclines, from oscillatory data and uses this information to construct low-dimensional dynamical models.

Zoom : 997 8258 4700 (pw : [REDACTED]

Impact of daylight saving time on physical activity patterns - Myna Lim
2026-05-08 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 “Impact of daylight saving time on physical activity patterns” by Hayoung Jeong et al., Nature Health, 2026.
Abstract
Daylight saving time (DST) remains contentious: some policymakers highlight behavioural benefits, while others emphasize health risks. Here we estimated the behavioural and physiological impacts of DST using longitudinal Fitbit measures from the National Institutes of Health All of Us Research Program. Avoiding strict modelling assumptions, we used a natural difference-in-differences design with Arizona (no DST) as a control against neighbouring Mountain Time states (observing DST). Contrary to common belief, DST transitions produced no net change in total daily steps. Instead, activity was reallocated to other times of day: fall transitions increased morning steps by 202 (confidence interval = [78, 326], P = 0.001) while reducing evening steps by 180 (confidence interval = [−263, −97], P < 0.001); spring transitions showed the opposite. Importantly, these treatment effects varied by demographics and across data-driven activity phenotypes (‘morning walker’, ‘neutral walker’ and ‘evening walker’). These disparities suggest that structural factors (for example, rigid work schedules, perceived safety) may constrain the capacity to flexibly adapt to time shifts for some populations. Physiologically, resting heart rate showed subtle intraday shifts mirroring behavioural changes, although differences were clinically insignificant. Our study provides a large-scale causal analysis of DST’s influence using continuous wearables data, illustrating how observational data can generate real-world evidence to inform health-relevant policies.

High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis - Hyeong Jun Jang
2026-05-15 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 “High-order Michaelis-Menten equations allow inference of hidden kinetic parameters in enzyme catalysis” by Divya Singh et al., Nat. Comm., 2025.
Abstract
Single-molecule measurements provide a platform for investigating the dynamical properties of enzymatic reactions. To this end, the single-molecule Michaelis-Menten equation was instrumental as it asserts that the first moment of the enzymatic turnover time depends linearly on the reciprocal of the substrate concentration. This, in turn, provides robust and convenient means to determine the maximal turnover rate and the Michaelis-Menten constant. Yet, the information provided by these parameters is incomplete and does not allow access to key observables such as the lifetime of the enzyme-substrate complex, the rate of substrate-enzyme binding, and the probability of successful product formation. Here we show that these quantities and others can be inferred via a set of high-order Michaelis-Menten equations that we derive. These equations capture universal linear relations between the reciprocal of the substrate concentration and distinguished combinations of turnover time moments, essentially generalizing the Michaelis-Menten equation to moments of any order. We demonstrate how key observables such as the lifetime of the enzyme-substrate complex, the rate of substrate-enzyme binding, and the probability of successful product formation, can all be inferred using these high-order Michaelis-Menten equations. We test our inference procedure to show that it is robust, producing accurate results with only several thousand turnover events per substrate concentration.

Heejung Shim - Modelling spatial transcriptomics: from flexible cell-type deconvolution to multi-scale spatial factor analysis
2026-05-18 12:30 - 13:30 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Abstract:
Spatial transcriptomics enables the study of gene expression within its spatial context, but introduces key statistical challenges, including mixed cellular composition and complex spatial structure. In this talk, I present two complementary modelling approaches.First, I introduce FlexiDeconv, a cell-type deconvolution method based on a modified Latent Dirichlet Allocation framework. A key feature of this method is its flexible use of reference information, allowing the model to balance prior information from scRNA-seq with signals from observed spatial data, and to adapt when the reference is incomplete or mismatched, a common challenge in practice.I then present WaviFM, a wavelet-based Bayesian sparse factor model that captures spatial gene expression patterns across multiple spatial scales, enabling the detection of both fine and broad spatial patterns. In addition, WaviFM can incorporate gene-set information to guide factor inference, while allowing for uncertainty and potential errors in these annotations.Together, these methods illustrate how flexible modelling of prior information and multi-scale modelling of spatial structure can improve our ability to extract biologically meaningful signals from spatial transcriptomics data.

Mathematics of diffusive signaling - Alan Lindsay
2026-05-22 11:00 - 12:00 | B232 Seminar Room, IBS, 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, 34126, Korea, Republic of
Diffusive transport is one of the most fundamental mechanisms by which information, mass, and chemical signals propagate in physical and biological systems. In many settings—ranging from cellular signaling to chemical sensing—communication is mediated by particles undergoing random motion and interacting with small, spatially localized targets. This talk explores the mathematical structures underlying diffusive signaling, emphasizing how geometry, stochasticity, and multiscale effects shape signal detection and reliability. Using tools from stochastic processes, partial differential equations, and asymptotic analysis, I will describe how seemingly microscopic features can exert a dominant influence on macroscopic signaling outcomes, and highlight recent progress on quantifying signal strength, timing, and variability in complex geometries.

Zoom : 997 8258 4700 (pw : [REDACTED]

Causal Generalist Medical AI - Hongtu Zhu
2026-05-27 16:00 - 17:00 | ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium), (pw: [REDACTED]
The rapid evolution of flexible and reusable artificial intelligence (AI) models is transforming medical science. This short course introduces Causal Generalist Medical AI (Causal GMAI)—a paradigm that integrates causal inference with generalist AI models to enhance interpretability, robustness, and generalizability in medical decision-making. Causal GMAI employs self-supervised, semi-supervised, and supervised learning on diverse multimodal datasets—including imaging, electronic health records, clinical trials, laboratory results, genomics, knowledge graphs, and medical text—to perform a wide range of tasks with minimal task-specific supervision. By embedding causal reasoning, these models go beyond prediction to infer underlying causal relationships, improving diagnostic accuracy, treatment recommendations, and personalized medicine. The course covers key technical components such as causal discovery, counterfactual reasoning, and domain adaptation, alongside real-world applications. We will also explore challenges in regulation, validation, and dataset curation to ensure clinical reliability and ethical deployment. Designed for researchers, clinicians, data scientists, and AI practitioners, this course provides a foundation for advancing the next generation of trustworthy and interpretable medical AI.

Zoom : 997 8258 4700 (pw : [REDACTED]

Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies - Chitaranjan Mahapatra
2026-05-29 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 “Circadian rhythm profiles derived from accelerometer measures of the sleep-wake cycle in two cohort studies” by Sam vidil et al., Nature Communications, 2025.
Abstract:
Accelerometers allow objective measures of dimensions (rest-activity rhythm (RAR), daytime activity, sleep, and chronotype) of the bio-behavioural manifestation of circadian rhythm (CR) using multiple metrics in large-scale studies. These dimensions are rarely examined together due to methodological challenges of using correlated data. To address this challenge, we propose a two-step approach consisting of data reduction of CR metrics using principal component analyses, followed by k-means clustering to identify groups of individuals with a similar profile using data from the Whitehall II (N = 3,991, mean age=69.4years) and UK Biobank (N = 54,995, mean age=67.5years) cohort studies. Our analyses identified nine CR clusters: two presented extreme (most robust/poorest) RAR and (highest/lowest) daytime activity, two robust RAR with opposite sleep profiles (longer and efficient/shorter and fragmented), one high-intensity physical activity, and four poor RAR (one characterised by late chronotype, two by low activity but opposite sleep profiles, and one by restless (agitated) sleep). The participants in these nine clusters differed on sociodemographic, behavioural and health-related factors. Findings were similar in these two independent cohort studies, highlighting the validity of our approach. Most previous studies have used only the RAR dimension of circadian rhythm, and here we show that this might be an oversimplification as demonstrated by nine clusters characterised by combinations of RAR, daytime activity, sleep, and chronotype. Our innovative approach demonstrates feasibility of using all dimensions to study the impact of circadian rhythm dysregulation on health.

Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations - Gyuyoung Hwang
2026-06-05 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 “Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations” by Zheng-Meng Zhai et al., Nature Communications, 2025.
Abstract:
In applications, an anticipated issue is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed? We address this challenge by developing a hybrid transformer and reservoir-computing scheme. The transformer is trained without using data from the target system, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system, and its output is further fed into a reservoir computer for predicting its long-term dynamics or the attractor. The proposed hybrid machine-learning framework is tested using various prototypical nonlinear systems, demonstrating that the dynamics can be faithfully reconstructed from reasonably sparse data. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the situation where training data do not exist and the observations are random and sparse.

Insulin resistance prediction from wearables and routine blood biomarkers - Dongju Lim
2026-06-12 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 “Insulin resistance prediction from wearables and routine blood biomarkers” by Ahmed A. Metwally et al., Nature, 2026.

Abstract:
Insulin resistance (IR), a primary precursor to type 2 diabetes, is characterized by impaired insulin action in tissues1. However, diagnostic methods remain expensive and inaccessible, which hinders early intervention2,3. Here we present the WEAR-ME study, a large, remotely conducted study of IR (n = 1,165 participants; median body mass index (BMI) = 28 kg m−2, median age = 45 years, median haemoglobin A1c (HbA1c) = 5.4%) that uses time-series data from wearable devices and routine blood biomarkers to train deep neural networks against a ground-truth measure of IR (homeostatic model assessment of IR; HOMA-IR). Using a HOMA-IR cut-off of 2.9, our multimodal model achieved robust performance (area under the receiver operating characteristic curve (AUROC) = 0.80, sensitivity = 76%, specificity = 84%) with data from wearable devices, together with demographic and routine blood biomarker data. To enhance the use of time-series data from wearables, we fine-tuned a wearable foundation model (WFM) pretrained on 40 million hours of sensor data. In an independent validation cohort (n = 72), a model integrating WFM-derived representations with demographic data surpassed a demographics-only baseline (AUROC = 0.75 versus 0.66). Moreover, adding WFM-derived representations to a model with demographics, fasting glucose and a lipid panel substantially improved performance, compared with an identical model without data from wearables (AUROC = 0.88 versus 0.76). We integrate IR prediction into a large language model to contextualize the results and facilitate personalized recommendations. This work establishes a scalable, accessible framework for the early detection of metabolic risk, which could enable timely lifestyle interventions to prevent progression to type 2 diabetes.
