Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments

TL;DR

This paper introduces a Bayesian calibration framework combined with sensitivity analysis for oscillating biological models, effectively addressing statistical challenges in high-dimensional biological data.

Contribution

It develops a novel Bayesian calibration method using advanced MCMC techniques and a new sensitivity analysis approach based on intervention posterior for oscillatory biochemical models.

Findings

Successfully calibrated circadian oscillation model in Neurospora crassa

Demonstrated efficient parameter inference with advanced MCMC

Provided insights into parameter influence on biological oscillations

Abstract

Understanding the oscillating behaviors that govern organisms' internal biological processes 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 relying on an advanced Markov chain Monte Carlo (MCMC) technique which can efficiently 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 using the obtained MCMC samples as a computational tool. The proposed framework is illustrated with circadian oscillations observed in a filamentous fungus, Neurospora crassa.