Spatio-temporal inference for circadian gene transcription

Most bodily functions follow a schedule set by our biological clock and the suprachiasmatic nucleus is the main pacemaker of these circadian rhythms. Imaging techniques allow for real-time recording of circadian protein concentration in organotypic tissues. We develop inferential methodology for bioimaging data of circadian gene expression by combining a recently proposed stochastic transcription model with a spatial model in a Bayesian hierarchical framework and estimate parameters using adaptive MCMC. A novel measure of the biological robustness of the time-keeping mechanism is proposed which enables investigation of the complex machinery through which our brains keep time.