기초과학연구원 의생명수학그룹
Abstract Mathematical models of biological systems, including neurons, often feature components that evolve on very different timescales. Mathematical analysis of these multi-timescale systems can be greatly simplified by partitioning them …
Abstract: Physiological needs evoke motivational drives to produce natural behaviours for survival. However, the temporally intertwined dynamics of need and motivation have made it challenging to differentiate these two components …
Abstract: Reconstruction of gene regulatory networks (GRNs) is a powerful approach to capture a prioritized gene set controlling cellular processes. In our previous study, we developed TENET a GRN reconstructor …
Abstract: Cell-to-cell variability in gene expression exists even in a homogeneous population of cells. Dissecting such cellular heterogeneity within a biological system is a prerequisite for understanding how a biological …
Abstract: Through the past decades, electrophysiological experiments have revealed that extracellular electrical potential of brain show diverse rhythmic activity. Called ‘Local Field Potential(LFP)’, those rhythmic activities are thought to reflect …
Abstract: The Downing lab investigates the intricate biophysical interactions between cells and their environment, elucidating their role in modulating adult cell behavior and phenotypic transitions via epigenetic regulation of gene …
I present time delay estimation problems in astronomy as a part of time delay cosmography to infer the Hubble constant, the current expansion rate of the Universe. Time delay cosmography …
Presentor(s) Mentor Talk title Jaehun Jeong Gyuyoung Hwang Analyzing coupled SCN cell frequencies of mammals for multi-step transcriptional model Hyunsuk Choo, Yonghee Lee Seok Joo Chae Development of a …
Abstract: A system of ordinary differential equations (ODEs) is one of the most widely used tools to describe a deterministic dynamical system. In general, such ODEs involve nonlinear equations, which make analysis of dynamical systems difficult. In this talk, we introduce Koopman theory, which offers a linear representation – not an approximation – of nonlinear dynamics. In particular, we present a data-driven algorithm to find such a linear representation
Abstract: Wearable biosensors measure physiological variables with high temporal resolution over multiple days and are increasingly employed in clinical settings, such as continuous glucose monitoring in diabetes care. Such datasets …