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

• URL: http://arxiv.org/abs/2001.04794v1
• Date: Mon, 13 Jan 2020 11:04:26 GMT
• Title: A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
• Authors: Francesco Bardozzo, Pietro Lio', Roberto Tagliaferri
• Abstract summary: In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. The identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example.

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