Abstract
Escherichia coli BL21(DE3) is an industrial model microbe for the mass-production of bioproducts such as biofuels, biorefineries, and recombinant proteins. However, despite its important role in scientific research and biotechnological applications, a high-quality metabolic network model for metabolic engineering is yet to be developed. Here, we present the comprehensive metabolic network model of E. coli BL21(DE3), named iHK1487, based on the latest genome reannotation and phenome analysis. The metabolic model consists of 1,164 unique metabolites, 2,701 metabolic reactions, and 1,487 genes. The model was validated and improved by comparing the simulation results with phenome data from phenotype microarray tests. Previous transcriptome profile data was incorporated during model reconstruction, and flux prediction was simulated using the model. iHK1487 was simulated to explore the metabolic features of BL21(DE3) such as broad spectrum amino acid utilization and enhanced flux through the upper glycolytic pathway and TCA cycle. iHK1487 will contribute to systematic understanding of cellular physiology and metabolism of E. coli BL21(DE3) and highlight its biotechnological applications.
Highlights
Modeling and simulation of metabolic networks are well-established computational tools for myriad applications such as designing of microbial cell factories, model-driven discovery, and phenotype prediction [1,2,3,4]
We utilized the metabolic model of K-2 MG1655 [25], which is the most comprehensive and accurate model in microorganisms, for reconstructing the BL21(DE3) metabolic model, starting with the identification of the gene set commonly present in BL21(DE3) and K-12 MG1655
The major genetic difference in the central metabolism between BL21 and K-12 strains is the lack of pgl, which encodes 6-phosphogluconolactonase (Pgl) that is involved in the oxidative branch of the pentose phosphate pathway (PPP) [27,64]
Summary
Modeling and simulation of metabolic networks are well-established computational tools for myriad applications such as designing of microbial cell factories, model-driven discovery, and phenotype prediction [1,2,3,4]. Reconstruction of a comprehensive and accurate metabolic network model is a time- and labor-consuming task. To tackle this problem, a protocol for genome-scale metabolic reconstruction was suggested [7]. Several methods have been developed to support model reconstruction in a (semi-) automatic manner [8,9,10]. These methods convert genome annotation into a genome-scale metabolic model.
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