High-throughput generation, optimization and analysis of genome-scale metabolic models

• Published in: Nature biotechnology Vol. 28; no. 9; pp. 977 - 982
• Main Authors: Henry, Christopher S, DeJongh, Matthew, Best, Aaron A, Frybarger, Paul M, Linsay, Ben, Stevens, Rick L
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01-09-2010; Nature Publishing Group
• Subjects: 631/114; 631/1647/1513; 631/326/41; 631/61/320; Agriculture; Bacteria - classification; Bacteria - genetics; Bacteria - metabolism; Bioinformatics; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; DNA sequencing; Genes, Bacterial; Genetic aspects; Genome, Bacterial - genetics; Genomes; Genomics; Genotype & phenotype; Genotypes; Identification and classification; Life Sciences; Metabolism; Methods; Models, Biological; Nucleotide sequencing; Optimization; Phenotype; resource; United States
• ISSN: 1087-0156; 1546-1696
• DOI: 10.1038/nbt.1672

Reconstructing a metabolic model from the genome sequence of an organism is a useful but arduous approach for predicting phenotypes. Henry
et al
. describe a resource that automates most of this process and apply it to create >100 new metabolic models of microbes.
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 ∼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.