기초과학연구원 의생명수학그룹
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
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 …
In this talk, we discuss the paper “Digital biomarkers for brain health: passive and continuous assessment from wearable sensors” by Igor Matias et al., npj digital medicine, 2026. Abstract Continuous and scalable monitoring of cognition and affective states is critical for the early detection of brain health, which is currently limited by the burden 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 …
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 …