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The genetic regulatory network (GRN) plays a key role in controlling theresponse of the cell to changes in the environment. Although the structure ofGRNs has been the subject of many studies, their large scale structure in thelight of feedbacks from the metabolic network (MN) has received relativelylittle attention. Here we study the causal structure of the GRNs, namely thechain of influence of one component on the other, taking into account feedbackfrom the MN. First we consider the GRNs of E. coli and B. subtilis withoutfeedback from MN and illustrate their causal structure. Next we augment theGRNs with feedback from their respective MNs by including (a) links from genescoding for enzymes to metabolites produced or consumed in reactions catalyzedby those enzymes and (b) links from metabolites to genes coding fortranscription factors whose transcriptional activity the metabolites alter bybinding to them. We find that the inclusion of feedback from MN into GRNsignificantly affects its causal structure, in particular the number of levelsand relative positions of nodes in the hierarchy, and the number and size ofthe strongly connected components (SCCs). We then study the functionalsignificance of the SCCs. For this we identify condition specific feedbacksfrom the MN into the GRN by retaining only those enzymes that are essential forgrowth in specific environmental conditions simulated via the technique of fluxbalance analysis (FBA). We find that the SCCs of the GRN augmented by thesefeedbacks can be ascribed specific functional roles in the organism. Ouralgorithmic approach thus reveals relatively autonomous subsystems withspecific functionality, or regulatory modules in the organism. This automatedapproach could be useful in identifying biologically relevant modules in otherorganisms for which network data is available, but whose biology is less wellstudied.

Feedbacks from the metabolic network to the genetic network reveal regulatory modules in E. coli and B. subtilis