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

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, op...

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Bibliographic Details
Published in:Nature biotechnology 2010-09, Vol.28 (9), p.977-982
Main Authors: Henry, Christopher S, DeJongh, Matthew, Best, Aaron A, Frybarger, Paul M, Linsay, Ben, Stevens, Rick L
Format: Article
Language:English
Subjects:
Bacteria - classification
Bacteria - genetics
Bacteria - metabolism
DNA sequencing
Genes, Bacterial
Genetic aspects
Genome, Bacterial - genetics
Genomes
Genomics
Genotype & phenotype
Identification and classification
Metabolism
Methods
Models, Biological
Nucleotide sequencing
Optimization
Phenotype
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Summary: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.
ISSN:1087-0156
1546-1696
DOI:10.1038/nbt.1672