title: Toward metabolic phenomics: analysis of genomic data using flux balances.
abstract: Small genome sequencing and annotations are leading to the definition of metabolic genotypes in an increasing number of organisms. Proteomics is beginning to give insights into the use of the metabolic genotype under given growth conditions. These data sets give the basis for systemically studying the genotype-phenotype relationship. Methods of systems science need to be employed to analyze, interpret, and predict this complex relationship. These endeavors will lead to the development of a new field, tentatively named phenomics. This article illustrates how the metabolic characteristics of annotated small genomes can be analyzed using flux balance analysis (FBA). A general algorithm for the formulation of in silico metabolic genotypes is described. Illustrative analyses of the in silico Escherichia coli K-12 metabolic genotypes are used to show how FBA can be used to study the capabilities of this strain.
authors.affiliation: Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA.
authors.forename: C H
authors.initials: CH
authors.lastname: Schilling
authors.forename: J S
authors.initials: JS
authors.lastname: Edwards
authors.forename: B O
authors.initials: BO
authors.lastname: Palsson
mesh_terms: D001709:Biotechnology; D003627:Data Interpretation, Statistical; D004926:Escherichia coli / Q000235:genetics / Q000378:metabolism; D015870:Gene Expression; D016678:Genome; D016680:Genome, Bacterial; D005838:Genotype; D008660:Metabolism; D008954:Models, Biological; D010641:Phenotype