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 ∼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.

DOI: 10.1038/nbt.1672
02004002011201220132014201520162017
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@article{Henry2010HighthroughputGO, title={High-throughput generation, optimization and analysis of genome-scale metabolic models}, author={Christopher S. Henry and Matthew DeJongh and Aaron A. Best and Paul Frybarger and Ben Linsay and Rick L. Stevens}, journal={Nature Biotechnology}, year={2010}, volume={28}, pages={977-982} }