Article

Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum

Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan. .
Microbial Cell Factories (Impact Factor: 4.25). 09/2009; 8:43. DOI: 10.1186/1475-2859-8-43
Source: PubMed

ABSTRACT In silico genome-scale metabolic models enable the analysis of the characteristics of metabolic systems of organisms. In this study, we reconstructed a genome-scale metabolic model of Corynebacterium glutamicum on the basis of genome sequence annotation and physiological data. The metabolic characteristics were analyzed using flux balance analysis (FBA), and the results of FBA were validated using data from culture experiments performed at different oxygen uptake rates.
The reconstructed genome-scale metabolic model of C. glutamicum contains 502 reactions and 423 metabolites. We collected the reactions and biomass components from the database and literatures, and made the model available for the flux balance analysis by filling gaps in the reaction networks and removing inadequate loop reactions. Using the framework of FBA and our genome-scale metabolic model, we first simulated the changes in the metabolic flux profiles that occur on changing the oxygen uptake rate. The predicted production yields of carbon dioxide and organic acids agreed well with the experimental data. The metabolic profiles of amino acid production phases were also investigated. A comprehensive gene deletion study was performed in which the effects of gene deletions on metabolic fluxes were simulated; this helped in the identification of several genes whose deletion resulted in an improvement in organic acid production.
The genome-scale metabolic model provides useful information for the evaluation of the metabolic capabilities and prediction of the metabolic characteristics of C. glutamicum. This can form a basis for the in silico design of C. glutamicum metabolic networks for improved bioproduction of desirable metabolites.

0 Followers
 · 
126 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We focus on the application of constraint-based methodologies and, more specifically, flux balance analysis in the field of metabolic engineering, and enumerate recent developments and successes of the field. We also review computational frameworks that have been developed with the express purpose of automatically selecting optimal gene deletions for achieving improved production of a chemical of interest. The application of flux balance analysis methods in rational metabolic engineering requires a metabolic network reconstruction and a corresponding in silico metabolic model for the microorganism in question. For this reason, we additionally present a brief overview of automated reconstruction techniques. Finally, we emphasize the importance of integrating metabolic networks with regulatory information-an area which we expect will become increasingly important for metabolic engineering-and present recent developments in the field of metabolic and regulatory integration.
    Journal of Industrial Microbiology and Biotechnology 01/2015; 42(3). DOI:10.1007/s10295-014-1576-3 · 2.51 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Genome-scale metabolic network model (GEM) is a fundamental framework in systems metabolic engineering. GEM is built upon extensive experimental data and literature information on gene annotation and function, metabolites and enzymes so that it contains all known metabolic reactions within an organism. Constraint-based analysis of GEM enables the identification of phenotypic properties of an organism and hypothesis-driven engineering of cellular functions to achieve objectives. Along with the advances in omics, high-throughput technology and computational algorithms, the scope and applications of GEM have substantially expanded. In particular, various computational algorithms have been developed to predict beneficial gene deletion and amplification targets and used to guide the strain development process for the efficient production of industrially important chemicals. Furthermore, an Escherichia coli GEM was integrated with a pathway prediction algorithm and used to evaluate all possible routes for the production of a list of commodity chemicals in E. coli. Combined with the wealth of experimental data produced by high-throughput techniques, much effort has been exerted to add more biological contexts into GEM through the integration of omics data and regulatory network information for the mechanistic understanding and improved prediction capabilities. In this paper, we review the recent developments and applications of GEM focusing on the GEM-based computational algorithms available for microbial metabolic engineering.
    Journal of Industrial Microbiology and Biotechnology 12/2014; 42(3). DOI:10.1007/s10295-014-1554-9 · 2.51 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The increasing need for the bio-based industrial production of compounds via microbial cell factories leads to a demand for computational pathway prediction tools. A variety of algorithms have been developed that can be used to identify possible metabolic pathways and their corresponding enzymatic parts. These prediction tools play a central role in metabolic pathway design and microbial chassis selection for industrial chemical production. Here, we briefly discuss how the development of some key computational tools, which are currently available for pathway construction, could facilitate the synthetic redesign of microbial chassis. Special emphasis is given to the characteristics and drawback(s) of some of the computational tools used in pathway prediction, and a generalized workflow for the design of microbial chemical factories is provided. Perspectives, challenges and future trends are briefly highlighted.
    02/2015; 2(1):1-14. DOI:10.3934/bioeng.2015.1.1

Preview (3 Sources)

Download
0 Downloads
Available from