Reconstruction and analysis of the industrial strain Bacillus megaterium WSH002 genome-scale in silico metabolic model

Zou, Wei; Zhou, M.; Liu, Li; Chen, Jian

doi:10.1016/j.jbiotec.2013.01.019

Cited by 26 publications

(24 citation statements)

References 37 publications

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“…reactions which are not, in reality, in its metabolism. Note that B. subtilis GEM had been used as a reference during the reconstruction of B. megaterium GEM (24). Therefore, some reactions/pathways which are present in B. subtilis but are actually absent in the metabolism of B. megaterium might have been wrongly included in its GEM.…”

Section: Discussionmentioning

confidence: 99%

The Food Crisis and New Technologies

Lotfi

2013

Iran J Biotech

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Section: Discussionmentioning

confidence: 99%

The Food Crisis and New Technologies

Lotfi

2013

Iran J Biotech

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“…Overexpressions of ZWF1, SOL3, MDH1, and GDH3 were positive for improved recombinant protein production, which demonstrated the high accuracy of prediction by GEM for heterologous enzyme production. Based on the successful application of GEMs in the metabolic engineering of model microorganism such as like E. coli and Saccharomyces cerevisiae (McCloskey et al 2013;Xu et al 2012Xu et al , 2013a, the GEMs of industrially important strains such as Candida glabrata, Ketogulonicigenium vulgare WSH-001, and Bacillus megaterium WSH002 have also been reconstructed (Xu et al 2013b;Zou et al 2012Zou et al , 2013. In silico metabolic engineering has further enhanced their cellular properties for industrial production.…”

Section: Gem-guided Metabolic Engineeringmentioning

confidence: 99%

Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects

Liu

Shin

et al. 2014

Appl Microbiol Biotechnol

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Metabolic engineering facilitates the rational development of recombinant bacterial strains for metabolite overproduction. Building on enormous advances in system biology and synthetic biology, novel strategies have been established for multivariate optimization of metabolic networks in ensemble, spatial, and dynamic manners such as modular pathway engineering, compartmentalization metabolic engineering, and metabolic engineering guided by genome-scale metabolic models, in vitro reconstitution, and systems and synthetic biology. Herein, we summarize recent advances in novel metabolic engineering strategies. Combined with advancing kinetic models and synthetic biology tools, more efficient new strategies for improving cellular properties can be established and applied for industrially important biochemical production.

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“…[4] Thus, in the light of current systems biology analysis, not only the individual physiological characteristics of the two strains, but also the interactions between them in this ecosystem have been the focus of research. [5,6] Studies have shown that the interactions between B. megaterium and K.vulgare are a synergistic combination of mutualism and antagonism. [7] By reconstructing genome-scale metabolic models of K. vulgare [8] and B. megaterium [9] based on genome annotation and data from the literature and biochemical databases, we found that K. vulgare was deficient in nutrient biosynthetic pathways.…”

Section: Introductionmentioning

confidence: 99%

Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network–genetic algorithm techniques

Yang

Gao

et al. 2015

Biotechnology & Biotechnological Equipment

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The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixedculture fermentation of 2-KGA. The root mean square error, R 2 and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g¢L ¡1 would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g¢L ¡1 ; urea, 10.2 g¢L ¡1 ; corn steep liquor, 16 g¢L ¡1 ; CaCO 3 , 3.96 g¢L ¡1 ; MgSO 4 , 0.28 g¢L ¡1 ). The 2-KGA production experimentally obtained using the ANNÀGA-designed medium was 71.21 § 1.53 g¢L ¡1 , which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.

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Reconstruction and analysis of the industrial strain Bacillus megaterium WSH002 genome-scale in silico metabolic model

Cited by 26 publications

References 37 publications

The Food Crisis and New Technologies

The Food Crisis and New Technologies

Toward metabolic engineering in the context of system biology and synthetic biology: advances and prospects

Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network–genetic algorithm techniques

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