Demand for analyzing very large datasets is increasing, especially with the introduction of chromatin immunoprecipitation sequencing which is a recent method of Next Generation Sequencing used to analyze protein interactions with DNA. The development of new technologies is revolutionizing genome-wide analysis and scientists' abilities to have a better understanding of the biological meaning but inferring gene regulatory networks from such data is still a major challenge in systems biology. Complex reactions at the molecular level in living cells and such knowledge, as it relates to specific phenotype, necessarily implies that a key molecular target should be considered within the framework of its gene regulatory network. The objective of our study is to explore the effect of proteins under specific conditions (e.g. treatment or starvation), in functional sub-pathways for specific phenotype. Using public microarray expression datasets for glioma and the KEGG human gene regulatory networks as proof of concept, we identified disrupted sub-paths due to STAT3 on functional glioma pathways. We expect that the proposed algorithmic approach could aid researchers to determine the biological relevance of the binding sites over functional sub-paths and provide insights for new disease treatments.
['Lefteris Koumakis', 'George Potamias', 'Kostas Marias', 'Manolis Tsiknakis']
An algorithmic approach for the effect of transcription factor binding sites over functional gene regulatory networks