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1.
Chinese Journal of Biotechnology ; (12): 1554-1564, 2022.
Article in Chinese | WPRIM | ID: wpr-927800

ABSTRACT

Graph-theory-based pathway analysis is a commonly used method for pathway searching in genome-scale metabolic networks. However, such searching often results in many pathways biologically infeasible due to the presence of currency metabolites (e.g. H+, H2O, CO2, ATP etc.). Several methods have been proposed to address the problem but up to now there is no well-recognized methods for processing the currency metabolites. In this study, we proposed a new method based on the function of currency metabolites for transferring of functional groups such as phosphate. We processed most currency metabolites as pairs rather than individual metabolites, and ranked the pairs based on their importance in transferring functional groups, in order to make sure at least one main metabolite link exists for any reaction. The whole process can be done automatically by programming. Comparison with existing approaches indicates that more biologically infeasible pathways were removed by our method and the calculated pathways were more reliable, which may facilitate the graph-theory-based pathway design and visualization.


Subject(s)
Genome , Metabolic Networks and Pathways
2.
Chinese Journal of Biotechnology ; (12): 1408-1420, 2022.
Article in Chinese | WPRIM | ID: wpr-927789

ABSTRACT

Ergothioneine is a multifunctional physiological cytoprotector, with broad application in foods, beverage, medicine, cosmetics and so on. Biosynthesis is an increasingly favored method in the production of ergothioneine. This paper summarizes the new progress in the identification of key pathways, the mining of key enzymes, and the development of natural edible mushroom species and high-yield engineering strains for ergothioneine biosynthesis in recent years. Through this review, we aim to reveal the molecular mechanism of ergothioneine biosynthesis and then employ the methods of fermentation engineering, metabolic engineering, and synthetic biology to greatly increase the yield of ergothioneine.


Subject(s)
Antioxidants , Ergothioneine/metabolism , Fermentation , Metabolic Engineering
3.
Chinese Journal of Biotechnology ; (12): 1390-1407, 2022.
Article in Chinese | WPRIM | ID: wpr-927788

ABSTRACT

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.


Subject(s)
Algorithms , Biosynthetic Pathways , Metabolic Engineering , Metabolic Networks and Pathways/genetics , Systems Biology
4.
Chinese Journal of Biotechnology ; (12): 531-545, 2022.
Article in Chinese | WPRIM | ID: wpr-927726

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.


Subject(s)
Genome , Metabolic Engineering , Metabolic Networks and Pathways/genetics , Models, Biological , Thermodynamics
5.
Chinese Journal of Biotechnology ; (12): 1914-1924, 2019.
Article in Chinese | WPRIM | ID: wpr-771743

ABSTRACT

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.


Subject(s)
Enzymes , Metabolism , Genome , Genetics , Metabolic Engineering , Metabolic Networks and Pathways , Genetics , Models, Biological , Thermodynamics
6.
Article in Chinese | WPRIM | ID: wpr-337404

ABSTRACT

Construction of artificial cell factory to produce specific compounds of interest needs wild strain to be genetically engineered. In recent years, with the reconstruction of many genome-scale metabolic networks, a number of methods have been proposed based on metabolic network analysis for predicting genetic modification targets that lead to overproduction of compounds of interest. These approaches use constraints of stoichiometry and reaction reversibility in genome-scale models of metabolism and adopt different mathematical algorithms to predict modification targets, and thus can discover new targets that are difficult to find through traditional intuitive methods. In this review, we introduce the principle, merit, demerit and application of various strain optimization methods in detail. The main problems in existing methods and perspectives on this emerging research field are also discussed, aiming to provide guidance to choose the appropriate methods according to different types of products and the reliability of the predicted results.


Subject(s)
Algorithms , Biotechnology , Methods , Computer Simulation , Genome , Industrial Microbiology , Metabolic Engineering , Methods , Metabolic Networks and Pathways , Models, Theoretical , Reproducibility of Results
7.
Article in Chinese | WPRIM | ID: wpr-242416

ABSTRACT

Kinetic model analysis is a useful tool for understanding the regulation and control of cellular metabolism and thus offering a guideline for rational design of high efficiency cell factory. Based on previously published models and experimental measurement of enzyme kinetics data, we developed a kinetic model for the threonine biosynthesis pathway in Escherichia coli. This model integrates the central pathways that produce precursors, ATP and reducing power with the threonine biosynthesis pathway from aspartate. In contrast to the previous models, we considered the energy and reducing power balance rather than artificially set their concentrations. Metabolic control analysis of the model showed that enzymes PTS, G6PDH, HDH etc. have great flux control coefficients on the threonine biosynthesis flux. This indicates higher threonine synthesis flux could be achieved by overexpressing these enzymes.


Subject(s)
Escherichia coli , Metabolism , Industrial Microbiology , Kinetics , Metabolic Networks and Pathways , Models, Biological , Threonine
8.
Chinese Journal of Biotechnology ; (12): 1173-1184, 2013.
Article in Chinese | WPRIM | ID: wpr-242491

ABSTRACT

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.


Subject(s)
Escherichia coli , Genetics , Metabolism , Genes, Bacterial , Genome, Bacterial , Genetics , Metabolic Engineering , Metabolic Networks and Pathways
9.
Chinese Journal of Biotechnology ; (12): 661-670, 2012.
Article in Chinese | WPRIM | ID: wpr-342452

ABSTRACT

High-throughput data supply a basis for the reconstruction of genome-scale metabolic networks, and meanwhile bring challenges to the reconstruction and analysis methods. With the increasing of data quantity, the time-consuming manual reconstruction and analysis are far behind the improvement of models. Therefore, various automatic methods emerge. The automatic reconstruction and analysis have irreplaceable effect in the standardization and programming of reconstruction and analysis methods, as well as largely improving the speed of reconstruction and understanding of the metabolic network. In this review, we introduced the progress of automatic reconstruction and the main analysis tools of genome-scale metabolic network. We further summarized the workflow of automatic reconstruction. The difficulties and perspectives on this research field are also discussed.


Subject(s)
Electronic Data Processing , Genome , Humans , Metabolic Networks and Pathways , Models, Theoretical , Software
10.
Chinese Journal of Biotechnology ; (12): 1340-1348, 2010.
Article in Chinese | WPRIM | ID: wpr-351588

ABSTRACT

Dozens of genome-scale metabolic networks have been reconstructed by integrating information from various databases on genes, proteins, metabolites and validated by experiment data from the literature. The reconstructed networks can be used to quantitatively investigate the interactions between components of a biological system at a system level. Such theoretical study could help us understand the organization principle of the large scale network and thus provide guidance to strain optimization through metabolic engineering technology. In this review, we evaluate the methods for the reconstruction, analysis and application of genome-scale metabolic networks. The difficulties and perspectives on this emerging research field are also discussed.


Subject(s)
Biotechnology , Methods , Genetic Engineering , Genomics , Methods , Industrial Microbiology , Metabolic Networks and Pathways , Physiology
11.
Article in Chinese | WPRIM | ID: wpr-404369

ABSTRACT

BACKGROUND:The non-steroidal antiinflammatory drugs (NSAIDs) were widely used to prevent heterotopic bone formation following total hip arthroplasty (THA),however,its efficacy and safety is poorly understood.OBJECTIVE:To determine the efficacy and safety of postoperative NSAIDs therapy in patients undergoing THA using Meta analysis.METHODS:The databases of PubMed,Embase,Cochrane Library,Chinese biomedical literature,CNKI,VIP as well as bibliographies of retrieved articles were researched for randomized controlled trials comparing NSAID versus control after THA,and the data were analyzed using Review Manager 5.0.RESULTS AND CONCLUSION:A total of 13 randomized controlled trials totaling 4706 participants were included.The result of meta analysis showed that low dose aspirin did not significantly affect the incidence of heterotopic bone formation (HBF) [RR=0.99,95% CI (0.87,1.14) rather than medium to high dose NSAIDs [RR=0.44,95% CI(0.30,0.64),there was no significant difference between two group in hip pain and physical function,the incidence of HBF was 16.0% in NSAID-group and 11.1% in 7 Gy group.Apart from low dose aspirin,medium to high doses of postoperative NSAIDs produce a substantial reduction in the incidence of HBF at the cost of minor high gastrointestinal side effect.Limited evidence showed there were no significant differences between the groups for improvements in hip pain and physical function,7 Gy fraction is more effective than use of NSAID.

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