A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities
ABSTRACTGenome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded,...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2024-02-01
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| Series: | mSystems |
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| Online Access: | https://journals.asm.org/doi/10.1128/msystems.00833-23 |
| _version_ | 1797302086571917312 |
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| author | Daniel Machado |
| author_facet | Daniel Machado |
| author_sort | Daniel Machado |
| collection | DOAJ |
| description | ABSTRACTGenome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications. |
| first_indexed | 2024-03-07T23:30:49Z |
| format | Article |
| id | doaj.art-9f3e4c54a25d4bb6b86491d93261cbbc |
| institution | Directory Open Access Journal |
| issn | 2379-5077 |
| language | English |
| last_indexed | 2024-03-07T23:30:49Z |
| publishDate | 2024-02-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | mSystems |
| spelling | doaj.art-9f3e4c54a25d4bb6b86491d93261cbbc2024-02-20T14:00:48ZengAmerican Society for MicrobiologymSystems2379-50772024-02-019210.1128/msystems.00833-23A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communitiesDaniel Machado0Department of Biotechnology and Food Science, Norwegian University of Science and Technology (NTNU), Trondheim, NorwayABSTRACTGenome-scale metabolic modeling is a powerful framework for predicting metabolic phenotypes of any organism with an annotated genome. For two decades, this framework has been used for the rational design of microbial cell factories. In the last decade, the range of applications has exploded, and new frontiers have emerged, including the study of the gut microbiome and its health implications and the role of microbial communities in global ecosystems. However, all the critical steps in this framework, from model construction to simulation, require the use of powerful linear optimization solvers, with the choice often relying on commercial solvers for their well-known computational efficiency. In this work, I benchmark a total of six solvers (two commercial and four open source) and measure their performance to solve linear and mixed-integer linear problems of increasing complexity. Although commercial solvers are still the fastest, at least two open-source solvers show comparable performance. These results show that genome-scale metabolic modeling does not need to be hindered by commercial licensing schemes and can become a truly open science framework for solving urgent societal challenges.IMPORTANCEModeling the metabolism of organisms and communities allows for computational exploration of their metabolic capabilities and testing their response to genetic and environmental perturbations. This holds the potential to address multiple societal issues related to human health and the environment. One of the current limitations is the use of commercial optimization solvers with restrictive licenses for academic and non-academic use. This work compares the performance of several commercial and open-source solvers to solve some of the most complex problems in the field. Benchmarking results show that, although commercial solvers are indeed faster, some of the open-source options can also efficiently tackle the hardest problems, showing great promise for the development of open science applications.https://journals.asm.org/doi/10.1128/msystems.00833-23genome-scale modelingmetabolismoptimization methods |
| spellingShingle | Daniel Machado A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities mSystems genome-scale modeling metabolism optimization methods |
| title | A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities |
| title_full | A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities |
| title_fullStr | A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities |
| title_full_unstemmed | A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities |
| title_short | A benchmark of optimization solvers for genome-scale metabolic modeling of organisms and communities |
| title_sort | benchmark of optimization solvers for genome scale metabolic modeling of organisms and communities |
| topic | genome-scale modeling metabolism optimization methods |
| url | https://journals.asm.org/doi/10.1128/msystems.00833-23 |
| work_keys_str_mv | AT danielmachado abenchmarkofoptimizationsolversforgenomescalemetabolicmodelingoforganismsandcommunities AT danielmachado benchmarkofoptimizationsolversforgenomescalemetabolicmodelingoforganismsandcommunities |