Figure 2
The proposed procedure for regulatory network inference. The procedure is divided into two phases: (a) The training phase involves learning the differential equation model parameters and inferring the unobserved TF protein activities on a sub-network of approximately known structure. By adopting a Bayesian inference procedure we can determine the posterior distribution over TF protein activities supported by the data. To close the system we place a Gaussian process prior distribution over the TF mRNA concentration functions[21]. (b) The prediction phase involves scoring all alternative regulation models for each putative target gene (2Imodels for I TFs). During this phase we assume that TF activities have a probability distribution given by the posterior distribution inferred during the training phase. The Bayesian evidence score is calculated for each regulation model and the posterior probability of any regulatory relationship of interest, such as TF–target gene associations, is determined by Bayesian model averaging.