title: A new network representation of the metabolism to detect chemical transformation modules.
abstract: BACKGROUND
  Metabolism 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).
  
  
  RESULTS
  RMS 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.
  
  
  CONCLUSIONS
  This 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.
authors.affiliation: Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. msorokina@genoscope.cns.fr.|CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France. msorokina@genoscope.cns.fr.|UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France. msorokina@genoscope.cns.fr.
authors.forename: Maria
authors.initials: M
authors.lastname: Sorokina
authors.affiliation: Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. cmedigue@genoscope.cns.fr.|CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France. cmedigue@genoscope.cns.fr.|UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France. cmedigue@genoscope.cns.fr.
authors.forename: Claudine
authors.initials: C
authors.lastname: Medigue
authors.affiliation: Direction des Sciences du Vivant, Commissariat à l'Energie Atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope, Laboratoire d'Analyses Bioinformatiques pour la Génomique et le Métabolisme, 2 rue Gaston Crémieux, Evry, 91057, France. vallenet@genoscope.cns.fr.|CNRS-UMR8030, 2 rue Gaston Crémieux, Evry, 91057, France. vallenet@genoscope.cns.fr.|UEVE, Université d'Evry Val d'Essonne, Boulevard François Mitterrand, Evry, 91057, France. vallenet@genoscope.cns.fr.
authors.forename: David
authors.initials: D
authors.lastname: Vallenet
mesh_terms: D016678:Genome; D053858:Metabolic Networks and Pathways; D008956:Models, Chemical; D005810:Multigene Family