Development of metabolic models with multiple constraints: a review.
- Author:
Xue YANG
1
;
Peiji ZHANG
1
;
Zhitao MAO
2
;
Xin ZHAO
2
;
Ruoyu WANG
2
;
Jingyi CAI
2
;
Zhiwen WANG
1
;
Hongwu MA
2
Author Information
- Publication Type:Review
- Keywords: bottleneck reaction; enzymatic constraints; genome-scale metabolic network models; key enzyme; metabolic models with multiple constraints (MCGEMs); thermodynamic constraints
- MeSH: Genome; Metabolic Engineering; Metabolic Networks and Pathways/genetics*; Models, Biological; Thermodynamics
- From: Chinese Journal of Biotechnology 2022;38(2):531-545
- CountryChina
- Language:Chinese
- Abstract: Constraint-based genome-scale metabolic network models (genome-scale metabolic models, GEMs) have been widely used to predict metabolic phenotypes. In addition to stoichiometric constraints, other constraints such as enzyme availability and thermodynamic feasibility may also limit the cellular phenotype solution space. Recently, extended GEM models considering either enzymatic or thermodynamic constraints have been developed to improve model prediction accuracy. This review summarizes the recent progresses on metabolic models with multiple constraints (MCGEMs). We presented the construction methods and various applications of MCGEMs including the simulation of gene knockout, prediction of biologically feasible pathways and identification of bottleneck steps. By integrating multiple constraints in a consistent modeling framework, MCGEMs can predict the metabolic bottlenecks and key controlling and modification targets for pathway optimization more precisely, and thus may provide more reliable design results to guide metabolic engineering of industrially important microorganisms.
