  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.

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