Using a dynamic bayesian network-based model for inference of escherichia coli SOS response pathway from gene expression data

Abstract

Largely due to the technological advances in bioinformatics, researchers are now garnering interests in inferring gene regulatory networks (GRNs) from gene expression data which is otherwise unfeasible in the past. This is because of the need of researchers to uncover the potentially vast information and understand the dynamic behavior of the GRNs. In this regard, dynamic Bayesian network (DBN) has been broadly utilized for the inference of GRNs thanks to its ability to handle time-series microarray data and modeling feedback loops. Unfortunately, the commonly found missing values in gene expression data, and excessive computation time owing to the large search space whereby all genes are treated as potential regulators for a target gene, often impede the effectiveness of DBN in inferring GRNs. This paper proposes a DBN-based model with missing values imputation to improve inference efficiency, and potential regulators selection which intends to decrease computation time by selecting potential regulators based on expression changes. We tested our proposed model on the Escherichia coli SOS response pathway which is responsible for repairing damaged DNA of the bacterium. The experimental results showed reduced computation time and improved efficiency in detecting gene-gene relationships