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• W4313252074 abstract "Abstract Bacillus subtilis is a well-characterized microorganism and a model for the study of Gram-positive bacteria. The bacterium can produce proteins at high densities and yields, which has made it valuable for industrial bioproduction. Like other cell factories, metabolic modeling of B. subtilis has discovered ways to optimize its metabolism toward various applications. The first genome-scale metabolic model (M-model) of B. subtilis was published more than a decade ago and has been applied extensively to understand metabolism, to predict growth phenotypes, and served as a template to reconstruct models for other Gram-positive bacteria. However, M-models are ill-suited to simulate the production and secretion of proteins as well as their proteomic response to stress. Thus, a new generation of metabolic models, known as metabolism and gene expression models (ME-models), has been initiated. Here, we describe the reconstruction and validation of a ME model of B. subtilis , i JT964-ME. This model achieved higher performance scores on the prediction of gene essentiality as compared to the M-model. We successfully validated the model by integrating physiological and omics data associated with gene expression responses to ethanol and salt stress. The model further identified the mechanism by which tryptophan synthesis is upregulated under ethanol stress. Further, we employed i JT964-ME to predict amylase production rates under two different growth conditions. We analyzed these flux distributions and identified key metabolic pathways that permitted the increase in amylase production. Models like i JT964-ME enable the study of proteomic response to stress and the illustrate the potential for optimizing protein production in bacteria." @default.
• W4313252074 created "2023-01-06" @default.
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• W4313252074 date "2022-12-27" @default.
• W4313252074 modified "2023-10-06" @default.
• W4313252074 title "Predicting stress response and improved protein overproduction in Bacillus subtilis" @default.
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• W4313252074 doi "https://doi.org/10.1038/s41540-022-00259-0" @default.
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• W4313252074 hasPublicationYear "2022" @default.
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