The characteristics, application and genome-scale metabolism network model iCW773 of bacillus glutamate.

Corynebacterium glutamicum is one of the most important industrial microorganisms and is widely used in the industrial production of amino acids, organic acids, vitamins and bioenecus.
as an industrial production strain, Bacillus glutamate has the characteristics of resistance to high-intensity fermentation robustness, strong environmental adaptability and so on.
the genome of the bacteria has been sequenced and the genetic operating system is constantly being refined.
At present, the metabolic engineering transformation of Bacillus glutamate mainly focuses on relieving feedback inhibition, weakening competitive pathways, enhancing the metabolic volume of target product anabolic pathways, and improving cell transport capacity, and so on, these strategies often fail to achieve the desired effect of metabolic volume changes, which is due to the lack of understanding of metabolic network regulation and expression regulation.
Genome-scale metabolic network model (Genome-scale metabolic model, GEM), which contains all known biochemical reactions occurring within cells, studies the interactions between components from the overall level of cells, analyzes the structure and function of metabolic networks, deeply understands and understands the regulatory mechanisms within cells at a global scale, and can guide efficient and targeted regulation of microbial metabolic networks to achieve specific physiological functions.
Therefore, it is urgent to establish a genome-scale metabolic network model of Bacillus glutamate, using computer simulation, quantitative analysis of metabolic passivity and qualitative analysis of cell growth dnotypes, to guide the metabolic engineering transformation of the products of the design purpose.
The Wen Tingyi Research Group of the Institute of Microbiology of the Chinese Academy of Sciences, in collaboration with the Tan Tianwei Research Group of Beijing University of Chemical Technology, has established a new genome-scale metabolic network model of Bacillus glutamate iCW773, which contains 773 genes, 950 metabolites and 1207 biochemical reactions.
By refining integrated reactions, balancing the charge and mass of metabolites in the reactive type, and using Gibbs' free energy rG' to limit the reaction direction, the network model can accurately simulate and predict the growth rate of Bacillus glutamate using different carbon sources and in grapes The distribution of intra-cell metabolic volume of sugar as the only carbon source was analyzed and compared with the metabolism of various network metabolic nodes between wild and high-yielding l-lysine, l-proline and l-serine engineering bacteria using the OptForcemust algorithm. The reclosement degree of the dosing interval, the metabolic engineering modification target and its transformation method are exactly the same as the experimental results, and the metabolic network of bacillus glutamate synthesis 1,2-propylene glycol and iso butanol was established by adding heterogenous synthesis. The metabolic modification target of propylene glycol and iso butanol synthesis proves the practicality of iCW773's metabolic network design for target products, and finally, a l-proline engineering bacteria is designed and constructed from the beginning by combining wet and dry method. Through continuous feeding of fermentation tanks, the production of l-proline in engineering bacteria reached 66.43 g/L, the conversion rate of glycic acid was 0.26 g/g, and the production intensity was 1.11 g/L/h, which is the highest conversion rate and production intensity reported for fermentation production of l-proline in the basic medium with glucose as the sole carbon source.
The genomic-scale metabolic network model constructed by the institute can provide an efficient research prediction platform, help to deepen the understanding of the metabolic network of Bacillus glutamate at the level of system biology, provide an efficient technical tool for the use of the bacteria as a cell factory synthesis of compounds with different metabolic pathways, and lay a research foundation for the design and transformation of synthetic networks of some bio-based chemicals.
The study was officially published on June 30 in Biotechnology for Biofuels, with Zhang Yu, Ph.D. student at the Institute of Microbiology, as the first author of the paper, Cai Jingyi, a doctoral student at the Beijing Academy of Chemical Sciences, and Tan Tianwei, a member of the Chinese Academy of Engineering, and Zhang Wei, an associate researcher, and Wen Tingyi, a researcher, as co-authors.
the study was conducted by the National High Technology Research and Development Program ("863" Program) (2014AA021203), the Chinese Academy of Sciences Science and Technology Services Network Program (STS Program) (KFJ-STS-QYZD-2 047 and KFJ-EW-STS-078) and the National Natural Science Foundation of China (31570074, 21390202 and 21436002).
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