Temporal inference of probabilistic Boolean networks

This paper presents a new method of fitting probabilistic Boolean networks (PBNs) to time-course state data. The critical issue to be addressed is to identify the contributions of the PBN´s constituent Boolean networks in a sequence of temporal data. The sequence must be partitioned into sections, each corresponding to a single model with fixed parameters. We propose an approach to subsequence identification based on ´purity functions´ derived from state transition tables, to be used in conjunction with a method for the identification of predictor genes and functions. We also present the estimation of the network switching probability, selection probabilities, perturbation rate, as well as observations on the inference of input genes, predictor functions and their relation with the length of the observed data sequence.