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BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale
Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. (13)C Metabolic Flux Analysis ((13)C MFA) is considered to be the gold standard for measuring metabolic fluxes. (13)C MFA typically wor...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664898/ https://www.ncbi.nlm.nih.gov/pubmed/37948450 http://dx.doi.org/10.1371/journal.pcbi.1011111 |
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author | Backman, Tyler W. H. Schenk, Christina Radivojevic, Tijana Ando, David Singh, Jahnavi Czajka, Jeffrey J. Costello, Zak Keasling, Jay D. Tang, Yinjie Akhmatskaya, Elena Garcia Martin, Hector |
author_facet | Backman, Tyler W. H. Schenk, Christina Radivojevic, Tijana Ando, David Singh, Jahnavi Czajka, Jeffrey J. Costello, Zak Keasling, Jay D. Tang, Yinjie Akhmatskaya, Elena Garcia Martin, Hector |
author_sort | Backman, Tyler W. H. |
collection | PubMed |
description | Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. (13)C Metabolic Flux Analysis ((13)C MFA) is considered to be the gold standard for measuring metabolic fluxes. (13)C MFA typically works by leveraging extracellular exchange fluxes as well as data from (13)C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the (13)C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in (13)C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from (13)C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-(13)C MOMA and P-(13)C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty. |
format | Online Article Text |
id | pubmed-10664898 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106648982023-11-10 BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale Backman, Tyler W. H. Schenk, Christina Radivojevic, Tijana Ando, David Singh, Jahnavi Czajka, Jeffrey J. Costello, Zak Keasling, Jay D. Tang, Yinjie Akhmatskaya, Elena Garcia Martin, Hector PLoS Comput Biol Research Article Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. (13)C Metabolic Flux Analysis ((13)C MFA) is considered to be the gold standard for measuring metabolic fluxes. (13)C MFA typically works by leveraging extracellular exchange fluxes as well as data from (13)C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the (13)C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in “non-gaussian” situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in (13)C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from (13)C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-(13)C MOMA and P-(13)C ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods by quantifying prediction uncertainty. Public Library of Science 2023-11-10 /pmc/articles/PMC10664898/ /pubmed/37948450 http://dx.doi.org/10.1371/journal.pcbi.1011111 Text en © 2023 Backman et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Backman, Tyler W. H. Schenk, Christina Radivojevic, Tijana Ando, David Singh, Jahnavi Czajka, Jeffrey J. Costello, Zak Keasling, Jay D. Tang, Yinjie Akhmatskaya, Elena Garcia Martin, Hector BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title | BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title_full | BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title_fullStr | BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title_full_unstemmed | BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title_short | BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale |
title_sort | bayflux: a bayesian method to quantify metabolic fluxes and their uncertainty at the genome scale |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10664898/ https://www.ncbi.nlm.nih.gov/pubmed/37948450 http://dx.doi.org/10.1371/journal.pcbi.1011111 |
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