title: High-throughput generation, optimization and analysis of genome-scale metabolic models.
abstract: Genome-scale metabolic models have proven to be valuable for predicting organism phenotypes from genotypes. Yet efforts to develop new models are failing to keep pace with genome sequencing. To address this problem, we introduce the Model SEED, a web-based resource for high-throughput generation, optimization and analysis of genome-scale metabolic models. The Model SEED integrates existing methods and introduces techniques to automate nearly every step of this process, taking approximately 48 h to reconstruct a metabolic model from an assembled genome sequence. We apply this resource to generate 130 genome-scale metabolic models representing a taxonomically diverse set of bacteria. Twenty-two of the models were validated against available gene essentiality and Biolog data, with the average model accuracy determined to be 66% before optimization and 87% after optimization.
authors.affiliation: Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois, USA. chenry@mcs.anl.gov
authors.forename: Christopher S
authors.initials: CS
authors.lastname: Henry
authors.forename: Matthew
authors.initials: M
authors.lastname: DeJongh
authors.forename: Aaron A
authors.initials: AA
authors.lastname: Best
authors.forename: Paul M
authors.initials: PM
authors.lastname: Frybarger
authors.forename: Ben
authors.initials: B
authors.lastname: Linsay
authors.forename: Rick L
authors.initials: RL
authors.lastname: Stevens
mesh_terms: D001419:Bacteria / Q000145:classification / Q000235:genetics* / Q000378:metabolism*; D005798:Genes, Bacterial; D016680:Genome, Bacterial / Q000235:genetics*; D008954:Models, Biological; D010641:Phenotype
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