Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?
To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capab...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/416289 |
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| author | Parizad Babaei Tahereh Ghasemi-Kahrizsangi Sayed-Amir Marashi |
| author_facet | Parizad Babaei Tahereh Ghasemi-Kahrizsangi Sayed-Amir Marashi |
| author_sort | Parizad Babaei |
| collection | DOAJ |
| description | To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139. |
| format | Article |
| id | doaj-art-67a7a849c81d48e58ef509f21f3075a3 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-67a7a849c81d48e58ef509f21f3075a32025-02-03T05:53:59ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/416289416289Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?Parizad Babaei0Tahereh Ghasemi-Kahrizsangi1Sayed-Amir Marashi2Department of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, IranDepartment of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, IranDepartment of Biotechnology, College of Science, University of Tehran, Tehran 1417614411, IranTo date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.http://dx.doi.org/10.1155/2014/416289 |
| spellingShingle | Parizad Babaei Tahereh Ghasemi-Kahrizsangi Sayed-Amir Marashi Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? The Scientific World Journal |
| title | Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? |
| title_full | Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? |
| title_fullStr | Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? |
| title_full_unstemmed | Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? |
| title_short | Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences? |
| title_sort | modeling the differences in biochemical capabilities of pseudomonas species by flux balance analysis how good are genome scale metabolic networks at predicting the differences |
| url | http://dx.doi.org/10.1155/2014/416289 |
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