title: Representation, simulation, and hypothesis generation in graph and logical models of biological networks.
abstract: This chapter presents a discussion of metabolic modeling from graph theory and logical modeling perspectives. These perspectives are closely related and focus on the coarse structure of metabolism, rather than the finer details of system behavior. The models have been used as background knowledge for hypothesis generation by Robot Scientists using yeast as a model eukaryote, where experimentation and machine learning are used to identify additional knowledge to improve the metabolic model. The logical modeling concept is being adapted to cell signaling and transduction biological networks.
authors.affiliation: Department of Computer Science, University of Aberystwyth, Ceredigion, UK. ken@seaview.gotadsl.co.uk
authors.forename: Ken
authors.initials: K
authors.lastname: Whelan
authors.forename: Oliver
authors.initials: O
authors.lastname: Ray
authors.forename: Ross D
authors.initials: RD
authors.lastname: King
mesh_terms: D019295:Computational Biology; D003196:Computer Graphics; D006801:Humans; D008128:Logic; D053858:Metabolic Networks and Pathways; D008954:Models, Biological; D010641:Phenotype