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

Zhitao Mao,Hongwu Ma,Qianqian Yuan,Jiawei Du,Xue Yang,Peiji Zhang,Xin Zhao

doi:10.3390/biom12010065

Abstract

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.

Highlights

Accurate prediction of metabolic phenotypes of an organism is a key goal of computational biology and has attracted more and more attention from researchers
In many cases, a microorganism shows suboptimal metabolism [5,6] that is inconsistent with the optimal solution of flux balance analysis (FBA) [7], implying that the metabolic capacity of an organism is constrained by other factors
We demonstrated that eciML1515 could simulate the sub-optimal metabolism such as overflow metabolism and the maximal growth rates under different carbon sources

Summary

Introduction

Accurate prediction of metabolic phenotypes of an organism is a key goal of computational biology and has attracted more and more attention from researchers. For this purpose, many genome-scale metabolic models have been developed [1,2] and successfully applied for guiding metabolic engineering based on flux balance analysis (FBA) and other stoichiometry-based methods [3,4]. Researchers proposed several new methods that introduced new constraints such as cell volume limitation [10], protein resource allocation [11], enzyme activity and total protein mass [12,13], thermodynamics [14] into the model along with the stoichiometric constraints.

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Discussion

Conclusion