Abstract
BackgroundIdentifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. In this work, we reformulate this problem as the optimization problems of computing a Minimum Dominating Set and a Minimum Connected Dominating Set for directed graphs.ResultsBoth MDS and MCDS are applied to the well-studied gene regulatory networks of the model organisms E. coli and S. cerevisiae and to a pluripotency network for mouse embryonic stem cells. The results show that MCDS can capture most of the known key player genes identified so far in the model organisms. Moreover, this method suggests an additional small set of transcription factors as novel key players for governing the cell-specific gene regulatory network which can also be investigated with regard to diseases. To this aim, we investigated the ability of MCDS to define key drivers in breast cancer. The method identified many known drug targets as members of the MDS and MCDS.ConclusionsThis paper proposes a new method to identify key player genes in gene regulatory networks. The Java implementation of the heuristic algorithm explained in this paper is available as a Cytoscape plugin at http://apps.cytoscape.org/apps/mcds. The SageMath programs for solving integer linear programming formulations used in the paper are available at https://github.com/maryamNazarieh/KeyRegulatoryGenesand as supplementary material.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0329-5) contains supplementary material, which is available to authorized users.
Highlights
Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis
Based on the directed form of the largest connected component (LCC), target genes are placed at the bottom level and a set of Transcription factor (TF) comprises the Minimum Connected Dominating Set (MCDS)
We found that the heuristic MCDS of the largest strongly connected component (LSCC) (80 genes) of this network contains 29 TFs
Summary
Identifying the gene regulatory networks governing the workings and identity of cells is one of the main challenges in understanding processes such as cellular differentiation, reprogramming or cancerogenesis. One particular challenge is to identify the main drivers and master regulatory genes that control such cell fate transitions. The term master regulatory gene was introduced by Susumu Ohno over 30 years ago According to his definition, a master regulator is a gene which stands at the top of a regulatory hierarchy and is not regulated by any other gene [10]. A master regulator is a gene which stands at the top of a regulatory hierarchy and is not regulated by any other gene [10] Later on, this term was redefined to involve a set of genes which either directly govern the particular cellular identity or are at the inception of developmental lineages and regulate a cascade of gene expressions to form specific lineages [11]
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