Visualization and Structure Learning of Gene Regulatory Networks using Bayesian Networks

Abstract

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). The gene regulatory networks are used to reveal the fundamental gene regulatory mechanisms, to determine the reasons for many diseases and interactions between drugs and their targets, to produce a clear and comprehensible notion for cell regulation,. The introduction of experimental technologies such as microarrays and chromatin immunoprecipitation ChIP-chip, has provided a large number of available datasets related to gene expression and transcription factors (TFs). These datasets are basis for further analysis to reveal the gene regulation mechanisms. We implemented and visualized the dynamic Bayesian network which is able to cope with missing data and can include a prior knowledge about transcription factors. Also, we describe the obtained results and survey the common structure learning algorithms for learning of GRN’s structure.