Dae Wook Kim, Revealing causes of disrupted wake-sleep cycles using mathematical model (BRIC Webinar)

Registration is required to attend this talk (link: https://www.ibric.org/seminar/#), and it will be presented in Korean. Abstract: 생체 시계 (Circadian clock)를 구성하는 핵심 단백질인 PERIOD (PER)의 양은 12시간 동안 증가했다가 12시간 동안 감소하며 24시간 주기로 변화한다. 이 24시간 주기의 PER 리듬이 우리 몸의 시계 역할을 하여 수면 시간 등 다양한 행동 및 생리 작용의 시간을

What is the role of oscillatory signals in intracellular systems?

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Oscillatory signals are ubiquitously observed in many different intracellular systems such as immune systems and DNA repair processes. While we know how oscillatory signals are created, we do not fully understand what a critical role they play to regulate signal pathway systems in cells. Recently by using a stochastic nucleosome system, we found that a

Deciphering circadian clock cell network morphology within the biological master clock, the suprachiasmatic nucleus

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract: The biological master clock, the suprachiasmatic nucleus (SCN) of a mouse, contains many (~20,000) clock cells heterogeneous, particularly with respect to their circadian period. Despite the inhomogeneity, within an intact SCN, they maintain a very high degree of circadian phase coherence, which is generally rendered visible as system-wide propagating phase waves. The phase coherence

Statistical Inference with Neural Network Imputation for Item Nonresponse

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract: We consider the problem of nonparametric imputation using neural network models. Neural network models can capture complex nonlinear trends and interaction effects, making it a powerful tool for predicting missing values under minimum assumptions on the missingness mechanism. Statistical inference with neural network imputation, including variance estimation, is challenging because the basis for function

Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract: Inference method for a stochastic target-mediated drug disposition model via ABC-MCMC In this study, we discuss model robustness. Model robustness is consistent performance over variations of parameters. We formulate a stochastic target-mediated drug (TMDD) model, one of the pharmacokinetic models, to capture bi-exponential drug decay in plasma. A stochastic process is used to account

Scalable Modeling Approaches in Systems Immunology

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract: Systems biology seeks to build quantitative predictive models of biological system behavior. Biological systems, such as the mammalian immune system, operate across multiple spatiotemporal scales with a myriad of molecular and cellular players. Thus, mechanistic, predictive models describing such systems need to address this multiscale nature. A general outstanding problem is to cope with

Bayesian model calibration and sensitivity analysis for oscillating biochemical experiments

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract: Most organisms exhibit various endogenous oscillating behaviors, which provides crucial information about how the internal biochemical processes are connected and regulated. Along with physical experiments, studying such periodicity of organisms often utilizes computer experiments relying on ordinary differential equations (ODE) because configuring the internal processes is difficult. Simultaneously utilizing both experiments, however, poses a

COVID19 – Mathematical Modeling and Machine Learning

B305 Seminar room, IBS 55 Expo-ro Yuseong-guDaejeon,

Abstract This presentation include the following two topics. First of all, we consider a spread model of COVID-19 with time-dependent parameters via deep learning. We developed a SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional model to confirm its convergent nature. Next, We also developed a

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