title: Representations of metabolic knowledge: pathways.
abstract: The automatic generation of drawings of metabolic pathways is a challenging problem that depends intimately on exactly what information has been recorded for each pathway, and on how that information is encoded. The chief contributions of the paper are a minimized representation for biochemical pathways called the predecessor list, and inference procedures for converting the predecessor list into a pathway-graph representation that can serve as input to a pathway-drawing algorithm. The predecessor list has several advantages over the pathway graph, including its compactness and its lack of redundancy. The conversion between the two representations can be formulated as both a constraint-satisfaction problem and a logical inference problem, whose goal is to assign directions to reactions, and to determine which are the main chemical compounds in the reaction. We describe a set of production rules that solves this inference problem. We also present heuristics for inferring whether the exterior compounds that are substrates of reactions at the periphery of a pathway are side or main compounds. These techniques were evaluated on 18 metabolic pathways from the EcoCyc knowledge base.
authors.affiliation: Artificial Intelligence Center, SRI International, Menlo Park, CA 94025, USA.
authors.forename: P D
authors.initials: PD
authors.lastname: Karp
authors.forename: S M
authors.initials: SM
authors.lastname: Paley
mesh_terms: D000818:Animals; D003198:Computer Simulation; D006801:Humans; D015398:Signal Transduction