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ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model
Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of m...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773657/ https://www.ncbi.nlm.nih.gov/pubmed/35053213 http://dx.doi.org/10.3390/biom12010065 |
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author | Mao, Zhitao Zhao, Xin Yang, Xue Zhang, Peiji Du, Jiawei Yuan, Qianqian Ma, Hongwu |
author_facet | Mao, Zhitao Zhao, Xin Yang, Xue Zhang, Peiji Du, Jiawei Yuan, Qianqian Ma, Hongwu |
author_sort | Mao, Zhitao |
collection | PubMed |
description | Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering. |
format | Online Article Text |
id | pubmed-8773657 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-87736572022-01-21 ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model Mao, Zhitao Zhao, Xin Yang, Xue Zhang, Peiji Du, Jiawei Yuan, Qianqian Ma, Hongwu Biomolecules Article Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering. MDPI 2022-01-02 /pmc/articles/PMC8773657/ /pubmed/35053213 http://dx.doi.org/10.3390/biom12010065 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mao, Zhitao Zhao, Xin Yang, Xue Zhang, Peiji Du, Jiawei Yuan, Qianqian Ma, Hongwu ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title | ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title_full | ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title_fullStr | ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title_full_unstemmed | ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title_short | ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model |
title_sort | ecmpy, a simplified workflow for constructing enzymatic constrained metabolic network model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8773657/ https://www.ncbi.nlm.nih.gov/pubmed/35053213 http://dx.doi.org/10.3390/biom12010065 |
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