The mutual relation between the regulatory interaction network and other data sources: application to the E. coli genome

We explored the mutual relation between the regulatory network and microarray expression compendium and functional relation between genes in E. coli. For this aim, we tried to detect modules in the regulatory network which may resemble combinatorial regulators by using Fisher exact test and Monte Carlo sampling. As both of the methods, Fisher exact test and Monte Carlo sampling, failed to find modules in the regulatory network, at the next attempt we tried to define a similarity measure for each pair of genes based on their common regulators; we called it co-regulatory similarity. PageRank value was used as a measure to assess the importance of a regulator in the regulatory network. Based on this measure, the more important regulators were happened to be the more global regulators. This facilitated to define the co-regulatory similarity measure between each pair of genes based on the PageRank value of their common regulators. In our definition, regulators with lower PageRank values (more local regulators) contribute more in the co-regulatory similarity of their targets. We showed this co-regulatory similarity measure exhibits high correlation with the observed co-expression on the microarray expression compendium. Based on this study we could conclude that the observed co-expressed modules are the effect of the structure of the whole regulatory network rather than a set of combinatorial regulators. Similarly, we could exhibit high association between functional similarity and regulatory similarity of genes. We also studied the mutual relation between the regulatory network as the controlling network and the other interaction networks with non-controlling roles, like protein-protein interaction network and cellular pathways, inside the cell. We demonstrated how the regulatory network can predict modularity inside the other networks with non-controlling role inside the cell.