Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information
Summary: The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome-scale models of cellular metabolism, an...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-12-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004222017229 |
| _version_ | 1798019274664574976 |
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| author | José Antonio Pereiro-Morejón Jorge Fernandez-de-Cossio-Diaz Roberto Mulet |
| author_facet | José Antonio Pereiro-Morejón Jorge Fernandez-de-Cossio-Diaz Roberto Mulet |
| author_sort | José Antonio Pereiro-Morejón |
| collection | DOAJ |
| description | Summary: The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome-scale models of cellular metabolism, and recent data-driven studies have suggested that in chemostat cultures of Escherichia coli (E. coli), the growth rate and uptake rates of limiting nutrients are the most informative observables. We propose the thesis that this can be explained by the chemostat dynamics, which typically drives nutrient-limited cultures toward observable metabolic states maximally restricted in the dimensions of those fluxes. A practical consequence is that relevant flux observables can now be replaced by culture parameters usually controlled. We test our model by using simulations, and then we apply it to E. coli experimental data where we evaluate the quality of the inference, comparing it to alternative formulations that rest on convex optimization. |
| first_indexed | 2024-04-11T16:38:11Z |
| format | Article |
| id | doaj.art-418cd9d83f9e4a86af29f9074d9c6cfe |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-11T16:38:11Z |
| publishDate | 2022-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-418cd9d83f9e4a86af29f9074d9c6cfe2022-12-22T04:13:45ZengElsevieriScience2589-00422022-12-012512105450Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum informationJosé Antonio Pereiro-Morejón0Jorge Fernandez-de-Cossio-Diaz1Roberto Mulet2Group of Complex Systems and Statistical Physics, Physics Faculty, University of Havana, San Lazaro y L, Vedado, La Habana 10400, Cuba; Biology Faculty, University of Havana, San Lazaro y L, Vedado, La Habana 10400, CubaLaboratory of Physics of the Ecole Normale Superieure, CNRS UMR 8023, PSL Research, 24 rue Lhomond, 75005 Paris, Ile de France, FranceGroup of Complex Systems and Statistical Physics, Physics Faculty, University of Havana, San Lazaro y L, Vedado, La Habana 10400, Cuba; Department of Theoretical Physics, University of Havana, San Lazaro y L, Vedado, La Habana 10400, Cuba; Corresponding authorSummary: The study of cellular metabolism is limited by the amount of experimental data available. Formulations able to extract relevant predictions from accessible measurements are needed. Maximum Entropy (ME) inference has been successfully applied to genome-scale models of cellular metabolism, and recent data-driven studies have suggested that in chemostat cultures of Escherichia coli (E. coli), the growth rate and uptake rates of limiting nutrients are the most informative observables. We propose the thesis that this can be explained by the chemostat dynamics, which typically drives nutrient-limited cultures toward observable metabolic states maximally restricted in the dimensions of those fluxes. A practical consequence is that relevant flux observables can now be replaced by culture parameters usually controlled. We test our model by using simulations, and then we apply it to E. coli experimental data where we evaluate the quality of the inference, comparing it to alternative formulations that rest on convex optimization.http://www.sciencedirect.com/science/article/pii/S2589004222017229BioinformaticsSystems biologyMetabolic flux analysis |
| spellingShingle | José Antonio Pereiro-Morejón Jorge Fernandez-de-Cossio-Diaz Roberto Mulet Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information iScience Bioinformatics Systems biology Metabolic flux analysis |
| title | Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information |
| title_full | Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information |
| title_fullStr | Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information |
| title_full_unstemmed | Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information |
| title_short | Inference of metabolic fluxes in nutrient-limited continuous cultures: A Maximum Entropy approach with the minimum information |
| title_sort | inference of metabolic fluxes in nutrient limited continuous cultures a maximum entropy approach with the minimum information |
| topic | Bioinformatics Systems biology Metabolic flux analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2589004222017229 |
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