Jae Kyoung Kim
Korea Advanced Institute of Science and Technology · 物理学・天文学
研究室紹介
Professor Jae Kyoung Kim's research lab specializes in computational systems biology and quantitative pharmacology, focusing on developing advanced mathematical models to understand complex biological dynamics—particularly in drug disposition and target engagement. The lab pioneers data-driven approaches for single-cell omics analysis, emphasizing automated, robust clustering and dimensionality reduction to uncover cellular heterogeneity. A central theme is the integration of systems-level modeling with experimental data to enable mechanistic, scale-agnostic insights into biological causality and drug response. The lab also advances theoretical frameworks such as causal emergence to bridge microscale molecular interactions with macroscale system behavior.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
5Abstract Competitive target-mediated drug disposition (competitive TMDD) arises when two drugs compete for the same target receptor. These dynamics can be characterized by the full competitive TMDD model; yet, its complexity motivated the use of a reduced model, which is invalid under high receptor concentrations. While this problem can be resolved by using the total quasi-steady state approximation (tQSSA), which remains valid for all receptor conditions, the exact formula of the tQSSA-based re
• Fully automated, data-driven clustering pipeline for scRNA-seq analysis. • Addresses the limitation of fixed principal component defaults in DR. • Data-driven pipeline improves clustering performance by 10-14%. • Performance gains are most pronounced on high-sparsity, high-skewness data. Single-cell RNA sequencing (scRNA-seq) provides deep insights into cellular heterogeneity but demands robust dimensionality reduction (DR) and clustering to handle high-dimensional, noisy data. Many DR and clu
Complex systems can often be analyzed at either the microscale of their individual components or the macroscale of their collective organization, yet it remains debated which level of description offers the most meaningful causal understanding. Hoel's recent study in Patterns addresses this challenge by introducing Causal Emergence 2.0, a novel formalization showing that a system's causal workings are best described by how causal influence is distributed across its hierarchy of scales.
Abstract Target-mediated drug disposition (TMDD) models have been widely used to describe nonlinear pharmacokinetic profiles driven by high-affinity, low-capacity drug–target binding. A pTMDD model, derived by applying the Padé approximation of the quasi-steady-state (QSS) model (qTMDD) was previously proposed. Although pTMDD model showed a comparable estimation accuracy while maintaining computational efficiency, further validation in realistic clinical scenarios and comprehensive performance e