Computationally efficient flux variability analysis
<p>Abstract</p> <p>Background</p> <p>Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling...
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| Format: | Article |
| Language: | English |
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BMC
2010-09-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/11/489 |
| _version_ | 1818756514898247680 |
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| author | Thiele Ines Gudmundsson Steinn |
| author_facet | Thiele Ines Gudmundsson Steinn |
| author_sort | Thiele Ines |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.</p> <p>Results</p> <p>We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.</p> <p>Conclusions</p> <p>Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.</p> |
| first_indexed | 2024-12-18T05:56:16Z |
| format | Article |
| id | doaj.art-8b72b0b499604ddba8ede6ff619718c3 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-18T05:56:16Z |
| publishDate | 2010-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-8b72b0b499604ddba8ede6ff619718c32022-12-21T21:18:47ZengBMCBMC Bioinformatics1471-21052010-09-0111148910.1186/1471-2105-11-489Computationally efficient flux variability analysisThiele InesGudmundsson Steinn<p>Abstract</p> <p>Background</p> <p>Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.</p> <p>Results</p> <p>We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.</p> <p>Conclusions</p> <p>Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.</p>http://www.biomedcentral.com/1471-2105/11/489 |
| spellingShingle | Thiele Ines Gudmundsson Steinn Computationally efficient flux variability analysis BMC Bioinformatics |
| title | Computationally efficient flux variability analysis |
| title_full | Computationally efficient flux variability analysis |
| title_fullStr | Computationally efficient flux variability analysis |
| title_full_unstemmed | Computationally efficient flux variability analysis |
| title_short | Computationally efficient flux variability analysis |
| title_sort | computationally efficient flux variability analysis |
| url | http://www.biomedcentral.com/1471-2105/11/489 |
| work_keys_str_mv | AT thieleines computationallyefficientfluxvariabilityanalysis AT gudmundssonsteinn computationallyefficientfluxvariabilityanalysis |