title: HinCyc: a knowledge base of the complete genome and metabolic pathways of H. influenzae.
abstract: We present a methodology for predicting the metabolic pathways of an organism from its genomic sequence by reference to a knowledge base of known metabolic pathways. We applied these techniques to the genome of H. influenzae by reference to the EcoCyc knowledge base to predict which of 81 metabolic pathways of E. coli are found in H. influenzae. The resulting prediction is a complex hypothesis that is presented in computer form as HinCyc: an electronic encyclopedia of the genes and metabolic pathways of H. influenzae. HinCyc connects the predicted genes, enzymes, enzyme-catalyzed reactions, and biochemical pathways in a WWW-accessible knowledge base to allow scientists to explore this complex hypothesis.
authors.affiliation: Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA. pkarp@ai.sri.com
authors.forename: P D
authors.initials: PD
authors.lastname: Karp
authors.forename: C
authors.initials: C
authors.lastname: Ouzounis
authors.forename: S
authors.initials: S
authors.lastname: Paley
mesh_terms: D003195:Computer Communication Networks; D004926:Escherichia coli / Q000235:genetics; D016680:Genome, Bacterial; D016679:Genome, Viral; D006193:Haemophilus influenzae / Q000235:genetics*; D008954:Models, Biological; D012984:Software