scholarly journals ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model

Biomolecules ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 65
Author(s):  
Zhitao Mao ◽  
Xin Zhao ◽  
Xue Yang ◽  
Peiji Zhang ◽  
Jiawei Du ◽  
...  

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.

Author(s):  
Zhitao Mao ◽  
Xin Zhao ◽  
Xue Yang ◽  
Peiji Zhang ◽  
Jiawei Du ◽  
...  

Genome-scale metabolic models (GEMs) have been widely used for phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space inaccessible. Inspired by previous studies that take 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 behaviors 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.


2009 ◽  
Vol 25 ◽  
pp. S337-S338
Author(s):  
H. Taymaz-Nikerel ◽  
P.J.T. Verheijen ◽  
A.E. Borujeni ◽  
J.J. Heijnen ◽  
W.M. van Gulik

2015 ◽  
Vol 06 (02) ◽  
pp. 120-130 ◽  
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Aqeel S. Jaber ◽  
Julius B. Odili

PLoS ONE ◽  
2018 ◽  
Vol 13 (8) ◽  
pp. e0202565 ◽  
Author(s):  
Ignace L. M. M. Tack ◽  
Philippe Nimmegeers ◽  
Simen Akkermans ◽  
Filip Logist ◽  
Jan F. M. Van Impe

Zebrafish ◽  
2019 ◽  
Vol 16 (4) ◽  
pp. 348-362 ◽  
Author(s):  
Leonie van Steijn ◽  
Fons J. Verbeek ◽  
Herman P. Spaink ◽  
Roeland M.H. Merks

2014 ◽  
Vol 10 (11) ◽  
pp. 3014-3021 ◽  
Author(s):  
Mahdieh Hadi ◽  
Sayed-Amir Marashi

We introduce a generic constraint-based model of cancer metabolism, which is able to successfully predict the metabolic phenotypes of cancer cells.


3 Biotech ◽  
2020 ◽  
Vol 10 (3) ◽  
Author(s):  
Mingzhu Huang ◽  
Yue Zhao ◽  
Rong Li ◽  
Weihua Huang ◽  
Xuelan Chen

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