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An analytic approximation of the feasible space of metabolic networks
Assuming a steady-state condition within a cell, metabolic fluxes satisfy an underdetermined linear system of stoichiometric equations. Characterizing the space of fluxes that satisfy such equations along with given bounds (and possibly additional relevant constraints) is considered of utmost import...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384209/ https://www.ncbi.nlm.nih.gov/pubmed/28382977 http://dx.doi.org/10.1038/ncomms14915 |
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author | Braunstein, Alfredo Muntoni, Anna Paola Pagnani, Andrea |
author_facet | Braunstein, Alfredo Muntoni, Anna Paola Pagnani, Andrea |
author_sort | Braunstein, Alfredo |
collection | PubMed |
description | Assuming a steady-state condition within a cell, metabolic fluxes satisfy an underdetermined linear system of stoichiometric equations. Characterizing the space of fluxes that satisfy such equations along with given bounds (and possibly additional relevant constraints) is considered of utmost importance for the understanding of cellular metabolism. Extreme values for each individual flux can be computed with linear programming (as flux balance analysis), and their marginal distributions can be approximately computed with Monte Carlo sampling. Here we present an approximate analytic method for the latter task based on expectation propagation equations that does not involve sampling and can achieve much better predictions than other existing analytic methods. The method is iterative, and its computation time is dominated by one matrix inversion per iteration. With respect to sampling, we show through extensive simulation that it has some advantages including computation time, and the ability to efficiently fix empirically estimated distributions of fluxes. |
format | Online Article Text |
id | pubmed-5384209 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-53842092017-04-23 An analytic approximation of the feasible space of metabolic networks Braunstein, Alfredo Muntoni, Anna Paola Pagnani, Andrea Nat Commun Article Assuming a steady-state condition within a cell, metabolic fluxes satisfy an underdetermined linear system of stoichiometric equations. Characterizing the space of fluxes that satisfy such equations along with given bounds (and possibly additional relevant constraints) is considered of utmost importance for the understanding of cellular metabolism. Extreme values for each individual flux can be computed with linear programming (as flux balance analysis), and their marginal distributions can be approximately computed with Monte Carlo sampling. Here we present an approximate analytic method for the latter task based on expectation propagation equations that does not involve sampling and can achieve much better predictions than other existing analytic methods. The method is iterative, and its computation time is dominated by one matrix inversion per iteration. With respect to sampling, we show through extensive simulation that it has some advantages including computation time, and the ability to efficiently fix empirically estimated distributions of fluxes. Nature Publishing Group 2017-04-06 /pmc/articles/PMC5384209/ /pubmed/28382977 http://dx.doi.org/10.1038/ncomms14915 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Braunstein, Alfredo Muntoni, Anna Paola Pagnani, Andrea An analytic approximation of the feasible space of metabolic networks |
title | An analytic approximation of the feasible space of metabolic networks |
title_full | An analytic approximation of the feasible space of metabolic networks |
title_fullStr | An analytic approximation of the feasible space of metabolic networks |
title_full_unstemmed | An analytic approximation of the feasible space of metabolic networks |
title_short | An analytic approximation of the feasible space of metabolic networks |
title_sort | analytic approximation of the feasible space of metabolic networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5384209/ https://www.ncbi.nlm.nih.gov/pubmed/28382977 http://dx.doi.org/10.1038/ncomms14915 |
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