MetNet: A two-level approach to reconstructing and comparing metabolic networks

• Published in: PloS one Vol. 16; no. 2; p. e0246962
• Main Authors: Cocco, Nicoletta, Llabrés, Mercè, Reyes-Prieto, Mariana, Simeoni, Marta
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.02.2021; Public Library of Science (PLoS)
• Subjects: Biology; Biology and Life Sciences; Computational biology; Computer and Information Sciences; Contraction; Decomposition; Enzymes; Genomes; Metabolic networks; Metabolic pathways; Metabolism; Metabolites; Metabonomic analysis; Methods; Organisms; Pathways; Physical Sciences; Similarity measures; Spain; Italy
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0246962

Metabolic pathway comparison and interaction between different species can detect important information for drug engineering and medical science. In the literature, proposals for reconstructing and comparing metabolic networks present two main problems: network reconstruction requires usually human intervention to integrate information from different sources and, in metabolic comparison, the size of the networks leads to a challenging computational problem. We propose to automatically reconstruct a metabolic network on the basis of KEGG database information. Our proposal relies on a two-level representation of the huge metabolic network: the first level is graph-based and depicts pathways as nodes and relations between pathways as edges; the second level represents each metabolic pathway in terms of its reactions content. The two-level representation complies with the KEGG database, which decomposes the metabolism of all the different organisms into "reference" pathways in a standardised way. On the basis of this two-level representation, we introduce some similarity measures for both levels. They allow for both a local comparison, pathway by pathway, and a global comparison of the entire metabolism. We developed a tool, MetNet, that implements the proposed methodology. MetNet makes it possible to automatically reconstruct the metabolic network of two organisms selected in KEGG and to compare their two networks both quantitatively and visually. We validate our methodology by presenting some experiments performed with MetNet.