High-throughput Generation, Optimization and Analysis of Genome-scale Metabolic Models
Document Type
Article
Publication Date
9-2010
Publication Source
Nature Biotechnology
Volume Number
28
Issue Number
9
First Page
977
Last Page
U22
Publisher
Nature Publishing Group
ISSN
1087-0156
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 similar to 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.
Recommended Citation
Published in: Nature Biotechnology, Volume 28, Issue 9, September 1, 2010, pages 977-U22. Copyright © 2010 Nature Publishing Group, New York, NY. The final published version is available at: http://www.nature.com/nbt/journal/v28/n9/full/nbt.1672.html
