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Understanding the oscillating behaviors that govern organisms' internalbiological processes requires interdisciplinary efforts combining bothbiological and computer experiments, as the latter can complement the former bysimulating perturbed conditions with higher resolution. Harmonizing the twotypes of experiment, however, poses significant statistical challenges due toidentifiability issues, numerical instability, and ill behavior in highdimension. This article devises a new Bayesian calibration framework foroscillating biochemical models. The proposed Bayesian model is estimatedrelying on an advanced Markov chain Monte Carlo (MCMC) technique which canefficiently infer the parameter values that match the simulated and observedoscillatory processes. Also proposed is an approach to sensitivity analysisbased on the intervention posterior. This approach measures the influence ofindividual parameters on the target process by using the obtained MCMC samplesas a computational tool. The proposed framework is illustrated with circadianoscillations observed in a filamentous fungus, Neurospora crassa.

Bayesian Model Calibration and Sensitivity Analysis for Oscillating Biological Experiments