An improved algorithm for flux variability analysis
Abstract Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is,...
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| Format: | Article |
| Language: | English |
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BMC
2022-12-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05089-9 |
| _version_ | 1797977204116684800 |
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| author | Dustin Kenefake Erick Armingol Nathan E. Lewis Efstratios N. Pistikopoulos |
| author_facet | Dustin Kenefake Erick Armingol Nathan E. Lewis Efstratios N. Pistikopoulos |
| author_sort | Dustin Kenefake |
| collection | DOAJ |
| description | Abstract Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving $$2n+1$$ 2 n + 1 linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than $$2n+1$$ 2 n + 1 LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than $$2n+1$$ 2 n + 1 LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem. |
| first_indexed | 2024-04-11T05:03:13Z |
| format | Article |
| id | doaj.art-83a73ddabd784bec8c2d69998ef2c3c2 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T05:03:13Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-83a73ddabd784bec8c2d69998ef2c3c22022-12-25T12:32:10ZengBMCBMC Bioinformatics1471-21052022-12-0123111410.1186/s12859-022-05089-9An improved algorithm for flux variability analysisDustin Kenefake0Erick Armingol1Nathan E. Lewis2Efstratios N. Pistikopoulos3Texas A &M Energy Institute, Texas A &M UniversityDepartment of Pediatrics, University of California, San DiegoDepartment of Pediatrics, University of California, San DiegoTexas A &M Energy Institute, Texas A &M UniversityAbstract Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving $$2n+1$$ 2 n + 1 linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than $$2n+1$$ 2 n + 1 LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than $$2n+1$$ 2 n + 1 LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.https://doi.org/10.1186/s12859-022-05089-9Flux variability analysisLinear programmingBiological systems engineering |
| spellingShingle | Dustin Kenefake Erick Armingol Nathan E. Lewis Efstratios N. Pistikopoulos An improved algorithm for flux variability analysis BMC Bioinformatics Flux variability analysis Linear programming Biological systems engineering |
| title | An improved algorithm for flux variability analysis |
| title_full | An improved algorithm for flux variability analysis |
| title_fullStr | An improved algorithm for flux variability analysis |
| title_full_unstemmed | An improved algorithm for flux variability analysis |
| title_short | An improved algorithm for flux variability analysis |
| title_sort | improved algorithm for flux variability analysis |
| topic | Flux variability analysis Linear programming Biological systems engineering |
| url | https://doi.org/10.1186/s12859-022-05089-9 |
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