Article

Shinfuku, Y.et al. Development and experimental verification of a genome-scale metabolic model for Corynebacterium glutamicum. Microb. Cell Fact.8, 43

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: 4.22). 09/2009; 8(1):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.

    • "These databases are critical to improve the accuracy of the prediction of cellular phenotypes . More than 100 genome-scale metabolic network models were constructed for a wide range of different microorganisms, including Saccharomyces cerevisiae (Förster et al., 2003), Corynebacterium glutamicum (Shinfuku et al., 2009), Mannheimia succiniciproducens (Kim et al., 2007), Bacillus subtilis (Henry et al., 2009), Clostridium acetobutylicum (Lee et al., 2008), Clostridium beijerinckii (Milne et al., 2011), Lactococcus lactis (Flahaut et al., 2013), Pichia pastoris (Sohn et al., 2010), Pseudomonas putida (Puchałka et al., 2008), and so on. Recently, the ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks in comparison to FBA (Tran et al., 2008). "
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    ABSTRACT: Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications.
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    • "Recently, in silico metabolic simulation has been developed to consider whole metabolic networks. A genome-scale metabolic model, which includes most of the metabolic reactions of the cell [21-23], can estimate the flux distribution of the whole metabolic network using flux balance analysis (FBA) [24,25] by assuming the steady states of metabolic reactions and maximizing objective functions such as cell growth [26,27]. This method can be used to simulate the effects of gene modifications on target production and identify candidate genes for metabolic engineering [28-30]. "
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    Full-text · Article · May 2014 · Microbial Cell Factories
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    • "Substrate uptake could be identified by decreasing glucose concentrations between plug flow ports, with a biomass-specific uptake rate of qGLC = 0.90 (± 0.17) g g-1 h-1, i.e. 5.0 (± 1.0) mmol g-1 h-1 during the feed phase. This value is higher than the typical aerobic substrate uptake capacity of C. glutamicum (own data) and reported microaerobic uptake rates [26] (both ca. 3 mmol g-1 h-1). Interestingly, in the PFR there was also a significant pH difference between the different ports, as is shown for the longer residence time (τ = 87 s) in Figure 4A. "
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