A method for analysing gene expression data temporal sequence using Probabalistic Boolean Networks

This paper describes a new method for analysing gene expression temporal data sequences using Probabilistic Boolean Networks. Switch-like phenomena within biological systems result in difficulty in the modelling of gene regulatory networks. To tackle this problem, we propose an approach based on so called `purity functions' to partition the data sequence into sections each corresponding to a single model with fixed parameters, and introduce a method based on reverse engineering for the identification of predictor genes and functions. Furthermore, based on the analysis of Macrophage gene regulation in the interferon pathway, we develop a new model extending the PBN concept for the inference of gene regulatory networks from gene expression time-course data under different biological conditions. In conjunction with this, a new approach based on constrained prediction and Coefficient of Determination to identify the model from real expression data is presented in the paper.