A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways

In this work a machine learning approach for identifying the multi-omicsmetabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in termof their multi-omics follows from the analysis of these circuits . This is a consequenceof the alternation of the omic values of codon usage and protein abundance along thecircuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features areshown as an example. 1 Background In the bacterial metabolic pathways, it is possible to identify different small circuitsthat lead from an intermediate compound to another. Each bacterial pathway could be considered as a highly specific directed graph that presents more than one multi-omic circuit (MOC).