Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data Export

@article{citeulike:3384889, abstract = {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.}, author = {Durot, Maxime and Le Fevre, Francois and de Berardinis, Veronique and Kreimeyer, Annett and Vallenet, David and Combe, Cyril and Smidtas, Serge and Salanoubat, Marcel and Weissenbach, Jean and Schachter, Vincent}, citeulike-article-id = {3384889}, citeulike-linkout-0 = {http://dx.doi.org/10.1186/1752-0509-2-85}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/18840283}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=18840283}, doi = {10.1186/1752-0509-2-85}, issn = {1752-0509}, journal = {BMC Systems Biology}, keywords = {aip, fba, gem}, number = {1}, pages = {85+}, posted-at = {2008-10-08 01:50:13}, priority = {3}, title = {Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data}, url = {http://dx.doi.org/10.1186/1752-0509-2-85}, volume = {2}, year = {2008} }

TY - JOUR ID - citeulike:3384889 L3 - citeulike-article-id:3384889 N2 - 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. IS - 1 JF - BMC Systems Biology SN - 1752-0509 TI - Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data VL - 2 SP - 85 KW - aip KW - fba KW - gem AU - Durot, Maxime AU - Le Fevre, Francois AU - de Berardinis, Veronique AU - Kreimeyer, Annett AU - Vallenet, David AU - Combe, Cyril AU - Smidtas, Serge AU - Salanoubat, Marcel AU - Weissenbach, Jean AU - Schachter, Vincent PY - 2008/// UR - http://dx.doi.org/10.1186/1752-0509-2-85 ER -