The regulation of the expression of genes involved in metabolic pathways, termed as metabolic regulation, is vital to construct efficient microbial cell factories. With the continuous breakthroughs in synthetic biology, the mining and artificial design of high-quality regulatory elements have substantially improved our ability to modify and regulate cellular metabolic networks and its activities. The research on metabolic regulation has also evolved from the static regulation of single genes to the intelligent and precise dynamic regulation at the systems level. This review briefly summarizes the advances of metabolic regulation technologies in the past 30 years.
Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Synthetic Biology

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.
Genome ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Models, Biological ; Thermodynamics

It is among the goals in metabolic engineering to construct microbial cell factories producing high-yield and high value-added target products, and an important solution is to design efficient synthetic pathway for the target products. However, due to the difference in metabolic capacity among microbial chassises, the available substrate and the yielded products are limited. Therefore, it is urgent to design related metabolic pathways to improve the production capacity. Existing metabolic engineering approaches to designing heterologous pathways are mainly based on biological experience, which are inefficient. Moreover, the yielded results are in no way comprehensive. However, systems biology provides new methods for heterologous pathway design, particularly the graph-based and constraint-based methods. Based on the databases containing rich metabolism information, they search for and uncover possible metabolic pathways with designated strategy (graph-based method) or algorithm (constraint-based method) and then screen out the optimal pathway to guide the modification of strains. In this paper, we reviewed the databases and algorithms for pathway design, and the applications in metabolic engineering and discussed the strengths and weaknesses of existing algorithms in practical application, hoping to provide a reference for the selection of optimal methods for the design of product synthesis pathway.
Algorithms ; Biosynthetic Pathways ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Systems Biology

Genome-scale metabolic network model (GSMM) is an extremely important guiding tool in the targeted modification of industrial microbial strains, which helps researchers to quickly obtain industrial microbes with specific traits and has attracted increasing attention. Here we reviewe the development history of GSMM and summarized the construction method of GSMM. Furthermore, the development and application of GSMM in industrial microorganisms are elaborated by using four typical industrial microorganisms (Bacillus subtilis, Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae) as examples. In addition, prospects in the development trend of GSMM are proposed.
Corynebacterium glutamicum/genetics* ; Escherichia coli/genetics* ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics*

The minimum life is one of the most important research topics in synthetic biology. Minimizing a genome while at the same time maintaining an optimal growth of the cells is one of the important research objectives in metabolic engineering. Here we propose a genome minimization method based on genome scale metabolic network analysis. The metabolic network is minimized by first deleting the zero flux reactions from flux variability analysis, and then by repeatedly calculating the optimal growth rates after combinatorial deletion of the non-essential genes in the reduced network. We applied this method to the classic E. coli metabolic network model ---iAF1260 and successfully reduced the number of genes in the model from 1 260 to 312 while maintaining the optimal growth rate unaffected. We also analyzed the metabolic pathways in the network with the minimized number of genes. The results provide some guidance for the design of wet experiments to obtain an E. coli minimal genome.
Escherichia coli ; genetics ; metabolism ; Genes, Bacterial ; Genome, Bacterial ; genetics ; Metabolic Engineering ; Metabolic Networks and Pathways

Genome-scale metabolic network model (GSMM) is becoming an important tool for studying cellular metabolic characteristics, and remarkable advances in relevant theories and methods have been made. Recently, various constraint-based GSMMs that integrated genomic, transcriptomic, proteomic, and thermodynamic data have been developed. These developments, together with the theoretical breakthroughs, have greatly contributed to identification of target genes, systems metabolic engineering, drug discovery, understanding disease mechanism, and many others. This review summarizes how to incorporate transcriptomic, proteomic, and thermodynamic-constraints into GSMM, and illustrates the shortcomings and challenges of applying each of these methods. Finally, we illustrate how to develop and refine a fully integrated GSMM by incorporating transcriptomic, proteomic, and thermodynamic constraints, and discuss future perspectives of constraint-based GSMM.
Genome/genetics* ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics* ; Models, Biological ; Proteomics

As an important model industrial microorganism, Escherichia coli has been widely used in pharmaceutical, chemical industry and agriculture. In the past 30 years, a variety of new strategies and techniques, including artificial intelligence, gene editing, metabolic pathway assembly, and dynamic regulation have been used to design, construct, and optimize E. coli cell factories, which remarkably improved the efficiency for biotechnological production of chemicals. In this review, three key aspects for constructing E. coli cell factories, including pathway design, pathway assembly and regulation, and optimization of global cellular performance, are summarized. The technologies that have played important roles in metabolic engineering of E. coli, as well as their future applications, are discussed.
Artificial Intelligence ; Escherichia coli/genetics* ; Gene Editing ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics*

Genome-scale metabolic network models have been successfully applied to guide metabolic engineering. However, the conventional flux balance analysis only considers stoichiometry and reaction direction constraints, and the simulation results cannot accurately describe certain phenomena such as overflow metabolism and diauxie growth on two substrates. Recently, researchers proposed new constraint-based methods to simulate the cellular behavior under different conditions more precisely by introducing new constraints such as limited enzyme content and thermodynamics feasibility. Here we review several enzyme-constrained models, giving a comprehensive introduction on the biological basis and mathematical representation for the enzyme constraint, the optimization function, the impact on the calculated flux distribution and their application in identification of metabolic engineering targets. The main problems in these existing methods and the perspectives on this emerging research field are also discussed. By introducing new constraints, metabolic network models can simulate and predict cellular behavior under various environmental and genetic perturbations more accurately, and thus can provide more reliable guidance to strain engineering.
Enzymes ; metabolism ; Genome ; genetics ; Metabolic Engineering ; Metabolic Networks and Pathways ; genetics ; Models, Biological ; Thermodynamics

Glutamic acid is an important amino acid with wide range of applications and huge market demand. Therefore, by performing transcriptome sequencing and re-sequencing analysis on Corynebacterium glutamicum E01 and high glutamate-producing strain C. glutamicum G01, we identified and selected genes with significant differences in transcription and gene levels in the central metabolic pathway that may have greatly influenced glutamate synthesis and further increased glutamic acid yield. The oxaloacetate node and α-ketoglutarate node play an important role in glutamate synthesis. The oxaloacetate node and α-ketoglutarate node were studied to explore effect on glutamate production. Based on the integrated strain constructed from the above experimental results, the growth rate in a 5-L fermenter was slightly lower than that of the original strain, but the glutamic acid yield after 48 h reached (136.1±5.53) g/L, higher than the original strain (93.53±4.52) g/L, an increase by 45.5%; sugar-acid conversion rate reached 58.9%, an increase of 13.7% compared to 45.2% of the original strain. The application of the above experimental strategy improved the glutamic acid yield and the sugar-acid conversion rate, and provided a theoretical basis for the metabolic engineering of Corynebacterium glutamicum.
Citric Acid Cycle ; Corynebacterium glutamicum/metabolism* ; Glutamic Acid/metabolism* ; Metabolic Engineering ; Metabolic Networks and Pathways/genetics*

Microbial cell factories capable of producing valuable chemicals from renewable feedstocks provide a promising alternative towards sustainability. However, environmental stress remarkably affects the performance of microbial cell factories. By extending the chronological lifespan of microbial cells, the performance of microbial cell factories can be greatly improved. Firstly, an evaluation system for chronological lifespan and semi-chronological lifespan was established based on the changes in survival rates. Secondly, the addition of anti-aging drugs such as cysteine, carnosine, aminoguanidine and glucosamine increased the chronological lifespan of E. coli by 80%, 80%, 50% and 120%, respectively. Finally, we demonstrated that extending the chronological lifespan of E. coli increased the yield of metabolites produced by E. coli cell factories with endogenous (lactic acid and pyruvic acid) or exogenous (malic acid) metabolic pathway by 30.0%, 25.0%, and 27.0%, respectively. The strategy of extending chronological lifespan of E. coli provides a potential approach for enhancing the performance of microbial cell factories.
Escherichia coli/genetics* ; Lactic Acid ; Longevity ; Metabolic Engineering ; Metabolic Networks and Pathways