Journal
NATURE BIOTECHNOLOGY
Volume 28, Issue 9, Pages 977-U22Publisher
NATURE RESEARCH
DOI: 10.1038/nbt.1672
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Funding
- US Department of Energy [DE-ACO2-06CH11357]
- National Institute of Allergy and Infectious Diseases [HHSN266200400042C]
- National Science Foundation [MCB-0745100, CCF-0829929, DBI-0850546]
- Argonne National Laboratory Guest Faculty
- United States Fulbright Scholarship Program
- Direct For Biological Sciences
- Div Of Biological Infrastructure [0850546] Funding Source: National Science Foundation
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [0829929] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [0851293] Funding Source: National Science Foundation
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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.
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