A new network representation of the metabolism to detect chemical transformation modules

Abstract

BackgroundMetabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).ResultsRMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation.ConclusionsThis representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0809-4) contains supplementary material, which is available to authorized users.

Metabolic pathway reconstruction strategies for central metabolism and natural product biosynthesis

“…Reactions in metabolic pathways do not occur at random: reaction sequences exhibit many conserved patterns, termed reaction modules [ 70 , 71 ]. The concept of “enzymatic reaction-likeness” can be generalized as “ k -step reaction sequence-likeness”, predicting how many ( k ) reactions are required to convert the starting compound into the goal compound [ 72 ].…”

Redes de regras de associação filtradas e multialvo

“…Redes Semânticas (MALLAT et al, 2015;KE et al, 2014) também tiveram suas abordagens evidenciadas, além de Redes Biológicas (HONG et al, 2018;FU et al, 2017;TSAI et al, 2017;Özgür Cingiz;BIRICIK;DIRI, 2017;DASGUPTA;SAHA, 2016;BELYI et al, 2016;SOROKINA;MEDIGUE;VALLENET, 2015;MORE et al, 2011) que embora seja uma aplicação e não uma modelagem proposta, ainda é definida como uma Rede.…”