Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models
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| Format: | Preprint |
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2026
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| _version_ | 1866917373171204096 |
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| author | Zafeiropoulos, Haris Garza, Daniel Rios |
| author_facet | Zafeiropoulos, Haris Garza, Daniel Rios |
| contents | Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective function to optimize, allowing for the exploration of the whole spectrum of the phenotypes a species can exhibit. However, sampling can also be restricted to a subspace where a chosen objective reaches at least a specified fraction of its optimum. This targeted approach adds value when investigating phenotypes that are optimal for a specific function. Contrary to Flux Balance Analysis, which returns a single solution, sampling leverages statistical power to uncover phenotypes that otherwise would be masked. This can be especially useful when changing the conditions (medium) in which a species lives. Here, we highlight some state-of-the-art methods for applying flux sampling at Genome-Scale Metabolic Models in different scenarios, and we showcase flux sampling applications |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_29546 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models Zafeiropoulos, Haris Garza, Daniel Rios Molecular Networks Quantitative Methods Flux sampling is an analysis that, based on a distribution, picks randomly an efficient number of points from the solution space of a metabolic model. Unlike most constraint-based analyses, flux sampling does not require an objective function to optimize, allowing for the exploration of the whole spectrum of the phenotypes a species can exhibit. However, sampling can also be restricted to a subspace where a chosen objective reaches at least a specified fraction of its optimum. This targeted approach adds value when investigating phenotypes that are optimal for a specific function. Contrary to Flux Balance Analysis, which returns a single solution, sampling leverages statistical power to uncover phenotypes that otherwise would be masked. This can be especially useful when changing the conditions (medium) in which a species lives. Here, we highlight some state-of-the-art methods for applying flux sampling at Genome-Scale Metabolic Models in different scenarios, and we showcase flux sampling applications |
| title | Sampling from the Solution Space and Metabolic Environments of Genome-Scale Metabolic Models |
| topic | Molecular Networks Quantitative Methods |
| url | https://arxiv.org/abs/2603.29546 |