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Activity Number: 99 - Applied Bayesian Methods in Sciences
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Korean International Statistical Society
Abstract #323679
Title: Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments
Author(s): Hang Joon Kim*
Companies: University of Cincinnati
Keywords: Circadian cycle; Differential equation; Generalized multiset sampler; Harmonic basis representation; Intervention posterior; Systematic biology

Most organisms exhibit various endogenous oscillating behaviors which provide crucial information as to how the internal biochemical processes are connected and regulated. Understanding the molecular mechanisms behind these oscillators requires interdisciplinary efforts combining both biological and computer experiments, as the latter can complement the former by simulating perturbed conditions with higher resolution. Harmonizing the two types of experiment, however, poses significant statistical challenges due to identifiability issues, numerical instability, and ill behavior in high dimension. This article devises a new Bayesian calibration framework for oscillating biochemical models. The proposed Bayesian model is estimated using an advanced MCMC which can effciently infer the parameter values that match the simulated and observed oscillatory processes. Also proposed is an approach to sensitivity analysis based on the intervention posterior. This approach measures the influence of individual parameters on the target process by utilizing the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous

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