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

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.

Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer

“…AutoPACMEN can automate database query and creation of models using sMOMENT (short metabolic modeling with enzyme kinetics), which introduces only one pseudo-reaction and pseudo-metabolite. Further improving upon these methods, ECMpy adds enzyme constraints without increasing model size ( 70 ). Studies have shown that adding protein constraints improves the accuracy of flux predictions by explaining suboptimal overflow metabolism and metabolic switches ( 69 , 70 ).…”

“…Further improving upon these methods, ECMpy adds enzyme constraints without increasing model size ( 70 ). Studies have shown that adding protein constraints improves the accuracy of flux predictions by explaining suboptimal overflow metabolism and metabolic switches ( 69 , 70 ). Instead of high-level protein constraints, the machinery cost of protein expression can be explicitly modeled using genome-scale models of metabolism and macromolecular expression (ME-models).…”

In silico cell factory design driven by comprehensive genome-scale metabolic models: development and challenges

“…Using a simplified workflow, enzymatic constrained metabolic network (ECMpy), the eciML1515 based on the iML1515 of E. coli was reconstructed in 2022. Unlike GECKO, it introduces enzyme constraints without modifying the existing metabolic reactions or adding new ones [7]. The simulation results of eciML1515 indicated that the acetate synthesis pathway is activated at higher growth rates than those of the oxidative respiration pathway because of its reduced enzyme cost [7].…”

“…Unlike GECKO, it introduces enzyme constraints without modifying the existing metabolic reactions or adding new ones [7]. The simulation results of eciML1515 indicated that the acetate synthesis pathway is activated at higher growth rates than those of the oxidative respiration pathway because of its reduced enzyme cost [7]. Using eciBAG597, it was predicted that the lactic acid synthesis rate of B. coagulans decreased at pH 5.5 compared with pH 6.0 because of to the ATP requirement of biomass growth and decrease in glucose absorption rate [46].…”

“…The addition of transcriptome, proteome, metabolome, and other parameters made GEMs more comprehensive and accurate in designing strains, analyzing potential pathways, and exploring mechanisms of interaction [3]. The enzyme constraint model was used to explain that E. coli secreted acetate and other byproducts at high specific growth rates due to the enzyme cost [7]. In addition, the reconstruction and application of GEMs became more convenient and efficient because of the advances in computing power and algorithms.…”