Thomas Philipp, Stochastic gene expression in lineage trees

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Stochasticity in gene expression is an important source of cell-to-cell variability (or noise) in clonal cell populations. So far, this phenomenon has been studied using the Gillespie Algorithm, or the Chemical Master Equation, which implicitly assumes that cells are independent and do neither grow nor divide. This talk will discuss recent developments in modelling

Nonparametric predictive model for sparse and irregular longitudinal data

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We propose a kernel-based estimator to predict the mean response trajectory for sparse and irregularly measured longitudinal data. The kernel estimator is constructed by imposing weights based on the subject-wise similarity on L2 metric space between predictor trajectories, where we assume that an analogous fashion in predictor trajectories over time would result in a similar

Hyeontae Jo,Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning”, Zhao, Shuai, et al., IEEE Transactions on Power Electronics 37.10 (2022): 11567-11578. Abstract Physics-informed machine learning (PIML) has been emerging as a promising tool for applications with domain knowledge and physical models. To uncover its potentials in power electronics, this article

Dongju Lim, Eui Min Jeong, Hyeontae Jo

Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

Dongju Lim: Mood Prediction for Bipolar Disorder Patient with Sleep Pattern Information   Eui Min Jeong:Noise attenuation through the multiple repression mechanism in transcription   Hyeontae Jo: Parameter estimation with discontinuously switching system

Eui Min Jung, Uncovering specific mechanisms across cell types in dynamical models

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “Uncovering specific mechanisms across cell types in dynamical models”, Hauber, Adrian Lukas, Marcus Rosenblatt, and Jens Timmer., bioRxiv (2023): 2023-01. Abstract Ordinary differential equations are frequently employed for mathematical modeling of biological systems. The identification of mechanisms that are specific to certain cell types is crucial for building useful models and to

Seonjin Kim, Nonparametric vs Parametric Regression

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

To understand nonparametric regression, we should know first what the parametric model is. Simply speaking, the parametric regression model consists of many assumptions and the nonparametric regression model eases the assumptions. I will introduce what assumptions the parametric regression model has and how the nonparametric regression model relieves them. In addition, their pros and cons will

Sushmita Roy, Deciphering gene regulatory networks underlying cell-fate specification

ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium) (pw: 1234)

Abstract: Cell fate specification is a dynamic process during which gene regulatory networks (GRNs) transition between different states and define cell type-specific patterns of gene expression. Identifying such cell type-specific gene regulatory networks is important for understanding how cells differentiate to diverse lineages from a progenitor state, how differentiated cells can be reprogrammed, and how

Seokjoo Chae, The energy cost and optimal design of networks for biological discrimination

B378 Seminar room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Korea, Republic of

We will discuss about “The energy cost and optimal design of networks for biological discrimination”, Yu, Qiwei, Anatoly B. Kolomeisky, and Oleg A. Igoshin., Journal of the Royal Society Interface 19.188 (2022): 20210883. Abstract Many biological processes discriminate between correct and incorrect substrates through the kinetic proofreading mechanism that enables lower error at the cost of

Hyun Kim

Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

TBD

Abbas Abbasli and Hyeongjun Jang

Tea Room, IBS Daejeon, Daejeon, Korea, Republic of

Abbas Abbasli: Accurate prediction of in-vivo drug interaction mediated by cytochrome P450 inhibition Hyeongjun Jang: Comparison of the inhibition constant approximation methods

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