title: An evolutionary approach for searching metabolic pathways.
abstract: Searching metabolic pathways that relate two compounds is a common task in bioinformatics. This is of particular interest when trying, for example, to discover metabolic relations among compounds clustered with a data mining technique. Search strategies find sequences to relate two or more states (compounds) using an appropriate set of transitions (reactions). Evolutionary algorithms carry out the search guided by a fitness function and explore multiple candidate solutions using stochastic operators. In this work we propose an evolutionary algorithm for searching metabolic pathways between two compounds. The operators and fitness function employed are described and the effect of mutation rate is studied. Performance of this algorithm is compared with two classical search strategies. Source code and dataset are available at http://sourceforge.net/projects/sourcesinc/files/eamp/
authors.affiliation: Center for Research and Development of Information Systems (CIDISI), National Scientific and Technical Research Council (CONICET), Argentina; Research Center for Signals, Systems and Computational Intelligence (Sinc(i)), FICH-UNL, National Scientific and Technical Research Council (CONICET), Argentina. Electronic address: mgerard@santafe-conicet.gov.ar.
authors.forename: Matias F
authors.initials: MF
authors.lastname: Gerard
authors.forename: Georgina
authors.initials: G
authors.lastname: Stegmayer
authors.forename: Diego H
authors.initials: DH
authors.lastname: Milone
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D053858:Metabolic Networks and Pathways / Q000235:genetics / Q000502:physiology; D008957:Models, Genetic
keywords: Evolutionary algorithms; Metabolic pathways; Search strategies