Genome scale inference of transcriptional regulatory networks using mutual information on complex interactions

Abstract

Inferring the genetic network architecture in cells is of great importance to biologists as it can lead to the understanding of cell signaling and metabolic dynamics underlying cellular processes, onset of diseases, and potential discoveries in drug development. The focus today has shifted to genome scale inference approaches using information theoretic metrics such as mutual information over the gene expression data. In this paper, we propose two classes of inference algorithms using scoring schemes on complex interactions which are primarily based on information theoretic metrics. The central idea is to go beyond pair-wise interactions and utilize more complex structures between any node (gene or transcription factor) and its possible multiple regulators (only transcription factors). While this increases the network inference complexity over pair-wise interaction based approaches, it achieves much higher accuracy. We restricted the complex interactions considered in this paper to 3-node structures (any node and its two regulators) to keep our schemes scalable to genome-scale inference and yet achieve higher accuracy than other state of the art approaches. Detailed performance analyses based on benchmark precision and recall metrics over the known Escherichia coli transcriptional regulatory network, indicated that the accuracy of the proposed algorithms (sCoIn, aCoIn and its variants) is consistently higher in comparison to popular algorithms such as context likelihood of relatedness (CLR), relevance networks (RN) and GEneNetwork Inference with Ensemble of trees (GENIE3).