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 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.

Automated summary

Genome-scale metabolic models (GEMs) are widely used for predicting the phenotypes of microorganisms. However, the lack of other constraints in the stoichiometric model often limits the accessibility of the metabolic solution space. In this study, we developed an enzyme-constrained metabolic network model (ECMpy) and applied it to Escherichia coli (E. coli) to improve the accuracy of phenotype predictions. By considering the total enzyme amount constraint, protein subunit composition, and enzyme kinetic parameters, we were able to better predict the overflow metabolism and growth rates of E. coli. The enzyme-constrained model revealed the tradeoff between enzyme usage efficiency and biomass yield, providing valuable insights for metabolic engineering.