Dataset Information

Addition of Escherichia coli K-12 growth observation and gene essentiality data to the EcoCyc database.

ABSTRACT: The sets of compounds that can support growth of an organism are defined by the presence of transporters and metabolic pathways that convert nutrient sources into cellular components and energy for growth. A collection of known nutrient sources can therefore serve both as an impetus for investigating new metabolic pathways and transporters and as a reference for computational modeling of known metabolic pathways. To establish such a collection for Escherichia coli K-12, we have integrated data on the growth or nongrowth of E. coli K-12 obtained from published observations using a variety of individual media and from high-throughput phenotype microarrays into the EcoCyc database. The assembled collection revealed a substantial number of discrepancies between the high-throughput data sets, which we investigated where possible using low-throughput growth assays on soft agar and in liquid culture. We also integrated six data sets describing 16,119 observations of the growth of single-gene knockout mutants of E. coli K-12 into EcoCyc, which are relevant to antimicrobial drug design, provide clues regarding the roles of genes of unknown function, and are useful for validating metabolic models. To make this information easily accessible to EcoCyc users, we developed software for capturing, querying, and visualizing cellular growth assays and gene essentiality data.

SUBMITTER: Mackie A

PROVIDER: S-EPMC3957686 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

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Addition of Escherichia coli K-12 growth observation and gene essentiality data to the EcoCyc database.

Mackie Amanda A Paley Suzanne S Keseler Ingrid M IM Shearer Alexander A Paulsen Ian T IT Karp Peter D PD

Journal of bacteriology 20131220 5

The sets of compounds that can support growth of an organism are defined by the presence of transporters and metabolic pathways that convert nutrient sources into cellular components and energy for growth. A collection of known nutrient sources can therefore serve both as an impetus for investigating new metabolic pathways and transporters and as a reference for computational modeling of known metabolic pathways. To establish such a collection for Escherichia coli K-12, we have integrated data o ...[more]

PMID: 24363340

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Project description:BackgroundConstraint-based models of Escherichia coli metabolic flux have played a key role in computational studies of cellular metabolism at the genome scale. We sought to develop a next-generation constraint-based E. coli model that achieved improved phenotypic prediction accuracy while being frequently updated and easy to use. We also sought to compare model predictions with experimental data to highlight open questions in E. coli biology.ResultsWe present EcoCyc-18.0-GEM, a genome-scale model of the E. coli K-12 MG1655 metabolic network. The model is automatically generated from the current state of EcoCyc using the MetaFlux software, enabling the release of multiple model updates per year. EcoCyc-18.0-GEM encompasses 1445 genes, 2286 unique metabolic reactions, and 1453 unique metabolites. We demonstrate a three-part validation of the model that breaks new ground in breadth and accuracy: (i) Comparison of simulated growth in aerobic and anaerobic glucose culture with experimental results from chemostat culture and simulation results from the E. coli modeling literature. (ii) Essentiality prediction for the 1445 genes represented in the model, in which EcoCyc-18.0-GEM achieves an improved accuracy of 95.2% in predicting the growth phenotype of experimental gene knockouts. (iii) Nutrient utilization predictions under 431 different media conditions, for which the model achieves an overall accuracy of 80.7%. The model's derivation from EcoCyc enables query and visualization via the EcoCyc website, facilitating model reuse and validation by inspection. We present an extensive investigation of disagreements between EcoCyc-18.0-GEM predictions and experimental data to highlight areas of interest to E. coli modelers and experimentalists, including 70 incorrect predictions of gene essentiality on glucose, 80 incorrect predictions of gene essentiality on glycerol, and 83 incorrect predictions of nutrient utilization.ConclusionSignificant advantages can be derived from the combination of model organism databases and flux balance modeling represented by MetaFlux. Interpretation of the EcoCyc database as a flux balance model results in a highly accurate metabolic model and provides a rigorous consistency check for information stored in the database.

Dataset Information

Addition of Escherichia coli K-12 growth observation and gene essentiality data to the EcoCyc database.

Publications

Addition of Escherichia coli K-12 growth observation and gene essentiality data to the EcoCyc database.

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