An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models
Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can...
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| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2014
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| Online Access: | http://hdl.handle.net/1721.1/90873 https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-2724-7228 |
| _version_ | 1826188220783656960 |
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| author | Chindelevitch, Leonid Trigg, Jason Regev, Aviv Berger Leighton, Bonnie |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Chindelevitch, Leonid Trigg, Jason Regev, Aviv Berger Leighton, Bonnie |
| author_sort | Chindelevitch, Leonid |
| collection | MIT |
| description | Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations. |
| first_indexed | 2024-09-23T07:56:23Z |
| format | Article |
| id | mit-1721.1/90873 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T07:56:23Z |
| publishDate | 2014 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/908732022-09-30T01:07:37Z An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models Chindelevitch, Leonid Trigg, Jason Regev, Aviv Berger Leighton, Bonnie Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Biology Massachusetts Institute of Technology. Department of Mathematics Chindelevitch, Leonid Trigg, Jason Berger, Bonnie Regev, Aviv Constraint-based models are currently the only methodology that allows the study of metabolism at the whole-genome scale. Flux balance analysis is commonly used to analyse constraint-based models. Curiously, the results of this analysis vary with the software being run, a situation that we show can be remedied by using exact rather than floating-point arithmetic. Here we introduce MONGOOSE, a toolbox for analysing the structure of constraint-based metabolic models in exact arithmetic. We apply MONGOOSE to the analysis of 98 existing metabolic network models and find that the biomass reaction is surprisingly blocked (unable to sustain non-zero flux) in nearly half of them. We propose a principled approach for unblocking these reactions and extend it to the problems of identifying essential and synthetic lethal reactions and minimal media. Our structural insights enable a systematic study of constraint-based metabolic models, yielding a deeper understanding of their possibilities and limitations. National Institutes of Health (U.S.) (Grant GM108348) Howard Hughes Medical Institute 2014-10-10T12:11:38Z 2014-10-10T12:11:38Z 2014-10 2014-06 Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/90873 Chindelevitch, Leonid, Jason Trigg, Aviv Regev, and Bonnie Berger. “An Exact Arithmetic Toolbox for a Consistent and Reproducible Structural Analysis of Metabolic Network Models.” Nature Communications 5 (October 7, 2014): 4893. https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-2724-7228 en_US http://dx.doi.org/10.1038/ncomms5893 Nature Communications Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Nature Publishing Group Nature |
| spellingShingle | Chindelevitch, Leonid Trigg, Jason Regev, Aviv Berger Leighton, Bonnie An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title | An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title_full | An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title_fullStr | An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title_full_unstemmed | An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title_short | An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| title_sort | exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models |
| url | http://hdl.handle.net/1721.1/90873 https://orcid.org/0000-0001-8567-2049 https://orcid.org/0000-0002-2724-7228 |
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