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.

翻译：理解支配生物体内在生物过程的振荡行为需要结合生物学与计算机实验的跨学科研究，因为后者能够通过更高分辨率的扰动条件模拟来补充前者。然而，由于可辨识性问题、数值不稳定性以及高维情况下的病态行为，协调这两类实验面临着显著的统计学挑战。本文为振荡生化模型设计了一种新的贝叶斯校准框架。所提出的贝叶斯模型依赖于一种先进的马尔可夫链蒙特卡洛（MCMC）技术进行估计，该技术能够高效推断出使模拟与观测振荡过程相匹配的参数值。同时，本文还提出了一种基于干预后验分布的灵敏度分析方法。该方法利用获得的MCMC样本作为计算工具，衡量各个参数对目标过程的影响。所提出的框架通过丝状真菌粗糙脉孢菌中观察到的昼夜节律振荡进行了实例验证。