Computational Methods for Inference of Gene Regulatory Networks from Gene Expression Data

Abstract

Gene regulatory networks specify the dependency interactions between genes through their transcriptional products. Understanding these interactions is very important since they decide the way cells behave, they may help diagnose certain health conditions and discover new drugs to cure the diseases. The construction of transcriptional gene regulatory networks by application of various mathematical and computational approaches to huge amount of genomics, proteomics and transcript-omics data, therefore, has emerged as one of the most promising methods of gaining insight into the working of human cells, tissues in terms of biological processes and disease dynamics. Here, we provide a review of various approaches that are used for the construction of gene regulatory networks. The details covered here pertain to properties of gene expression data, various approaches that are being used for the purpose of construction of gene regulatory networks from gene expression data.