A Composite Entropy Model in a Multiobjective Framework for Gene Regulatory Networks

Background: Transcription Factors (TFs) play a pivotal role in a Gene Regulatory Network (GRN) by differentially regulating genes across conditions. In some cases, it requires coordinated regulation of multiple TFs to control a Differentially Expressed (DE) gene. In this line, we have also developed simple architectures to unveil the parallel regulatory control by TFs. Objective: To date there are few works that have conducted active research to develop serial TF regulatory paths. In order to make some contribution in this specific area, here we have proposed an algorithm which puts up an architecture of multiple serial TF regulatory paths for a target gene. Methods: In order to explore the full potential of our algorithm we have tested it on one synthetic and three eukaryotic organism gene expression datasets. We were able to construct multiple transcription factor regulatory paths with varying lengths to each target differentially expressed gene with such transcription factors distributed across various multiobjective optimal fronts based on their regulatory properties. This is followed by multiple stage minimal entropy analysis. Conclusion: Through this multiple stage composite entropy approach we have not only assessed the strength of transcription factor to target interaction pathways supported by different literatures but added some new interactions and deleted a few existing ones having weak regulatory control probabilities.