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Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data
BACKGROUND: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respecti...
Autores principales: | , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2606687/ https://www.ncbi.nlm.nih.gov/pubmed/18840283 http://dx.doi.org/10.1186/1752-0509-2-85 |
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author | Durot, Maxime Le Fèvre, François de Berardinis, Véronique Kreimeyer, Annett Vallenet, David Combe, Cyril Smidtas, Serge Salanoubat, Marcel Weissenbach, Jean Schachter, Vincent |
author_facet | Durot, Maxime Le Fèvre, François de Berardinis, Véronique Kreimeyer, Annett Vallenet, David Combe, Cyril Smidtas, Serge Salanoubat, Marcel Weissenbach, Jean Schachter, Vincent |
author_sort | Durot, Maxime |
collection | PubMed |
description | BACKGROUND: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species. RESULTS: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps. CONCLUSION: The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model. |
format | Text |
id | pubmed-2606687 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26066872008-12-23 Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data Durot, Maxime Le Fèvre, François de Berardinis, Véronique Kreimeyer, Annett Vallenet, David Combe, Cyril Smidtas, Serge Salanoubat, Marcel Weissenbach, Jean Schachter, Vincent BMC Syst Biol Research Article BACKGROUND: Genome-scale metabolic models are powerful tools to study global properties of metabolic networks. They provide a way to integrate various types of biological information in a single framework, providing a structured representation of available knowledge on the metabolism of the respective species. RESULTS: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil bacterium of interest for environmental and biotechnological applications with large-spectrum biodegradation capabilities. Following initial reconstruction from genome annotation and the literature, we iteratively refined the model by comparing its predictions with the results of large-scale experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) large-scale growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262 were initially consistent with our experimental observations. Inconsistencies were systematically examined, leading in 65 cases to model corrections. The predictions of the final version of the model, which included three rounds of refinements, are consistent with the experimental results for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3) 94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we provide a web interface allowing online predictions and visualization of results on metabolic maps. CONCLUSION: The iterative reconstruction procedure led to significant model improvements, showing that genome-wide mutant phenotypes on several media can significantly facilitate the transition from genome annotation to a high-quality model. BioMed Central 2008-10-07 /pmc/articles/PMC2606687/ /pubmed/18840283 http://dx.doi.org/10.1186/1752-0509-2-85 Text en Copyright © 2008 Durot et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Durot, Maxime Le Fèvre, François de Berardinis, Véronique Kreimeyer, Annett Vallenet, David Combe, Cyril Smidtas, Serge Salanoubat, Marcel Weissenbach, Jean Schachter, Vincent Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title | Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title_full | Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title_fullStr | Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title_full_unstemmed | Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title_short | Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data |
title_sort | iterative reconstruction of a global metabolic model of acinetobacter baylyi adp1 using high-throughput growth phenotype and gene essentiality data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2606687/ https://www.ncbi.nlm.nih.gov/pubmed/18840283 http://dx.doi.org/10.1186/1752-0509-2-85 |
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