Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Mao, Zhitao, Zhao, Xin, Yang, Xue, Zhang, Peiji, Du, Jiawei, Yuan, Qianqian, Ma, Hongwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
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
_version_ 1784636147313410048
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
work_keys_str_mv AT maozhitao ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT zhaoxin ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT yangxue ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT zhangpeiji ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT dujiawei ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT yuanqianqian ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel
AT mahongwu ecmpyasimplifiedworkflowforconstructingenzymaticconstrainedmetabolicnetworkmodel