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Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in g...

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Autores principales: Xu, Zixiang, Sun, Jibin, Wu, Qiaqing, Zhu, Dunming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5725421/
https://www.ncbi.nlm.nih.gov/pubmed/29229946
http://dx.doi.org/10.1038/s41598-017-17552-2
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author Xu, Zixiang
Sun, Jibin
Wu, Qiaqing
Zhu, Dunming
author_facet Xu, Zixiang
Sun, Jibin
Wu, Qiaqing
Zhu, Dunming
author_sort Xu, Zixiang
collection PubMed
description Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can’t be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.
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spelling pubmed-57254212017-12-13 Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks Xu, Zixiang Sun, Jibin Wu, Qiaqing Zhu, Dunming Sci Rep Article Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can’t be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset. Nature Publishing Group UK 2017-12-11 /pmc/articles/PMC5725421/ /pubmed/29229946 http://dx.doi.org/10.1038/s41598-017-17552-2 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Xu, Zixiang
Sun, Jibin
Wu, Qiaqing
Zhu, Dunming
Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title_full Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title_fullStr Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title_full_unstemmed Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title_short Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
title_sort find_tfsbp: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5725421/
https://www.ncbi.nlm.nih.gov/pubmed/29229946
http://dx.doi.org/10.1038/s41598-017-17552-2
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