title: Predicting novel pathways in genome-scale metabolic networks.
abstract: Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.
authors.affiliation: Department of Bioinformatics, School of Biology and Pharmacy, University of Jena, Ernst-Abbe-Pl. 2, D-07743 Jena, Germany. stefan.schu@uni-jena.de
authors.forename: Stefan
authors.initials: S
authors.lastname: Schuster
authors.forename: Luís F
authors.initials: LF
authors.lastname: de Figueiredo
authors.forename: Christoph
authors.initials: C
authors.lastname: Kaleta
mesh_terms: D019295:Computational Biology / Q000379:methods*; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D049490:Systems Biology / Q000379:methods*