Bayesian networks application for representation and structure learning of gene regulatory networks

The cell functions and development are regulated by complex networks of genes, proteins and other components by means of their mutual interactions. These networks are called gene regulatory networks (GRNs). GRNs are used to reveal the fundamental gene regulatory mechanisms, to determine the reasons for many diseases and interactions between drugs and their targets. The introduction of experimental technologies such as microarrays, ChIP-chip which combines chromatin immunoprecipitation (ChIP) with microarrays and ChIP-Seq which combines ChIP with DNA sequencing, has provided a large number of available datasets related to gene expression and transcription factors (TFs) and their interactions. These datasets are basis for further analysis to reveal the gene regulation mechanisms. Many models have been applied to represent gene regulatory networks. We have used the dynamic Bayesian network model which is able to cope with missing data and can include a prior knowledge about transcription factors and their activation/inhibition of corresponding genes. We describe the obtained results and survey the common structure learning algorithms for learning of GRN's structure. We tested the obtained GRN for test datasets with different sizes and in the paper describe obtained dependencies between the ratio of Bayesian score and BIC and dataset size.