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Model-driven in Silico glpC Gene Knockout Predicts Increased Succinate Production from Glycerol in Escherichia Coli

Bioinformatics Research Group (BIRG), Department of Biosciences and Health Sciences, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor Bahru. Malaysia

Metabolic engineered targeting for increased succinate production in Escherichia coli using glycerol as a low cost carbon source has attracted global attention in recent years. Succinate production in engineered E. coli has progressed significantly using an experimental trial and error approach. The use of a model-guided, targeted metabolic gene knockout prediction for increased succinate production from glycerol under anaerobic conditions in E. coli still remains largely underexplored. In this study, we applied a model-driven, targeted glpC/b2243 in silico metabolic gene knockout using E. coli genome scale model iJO1366 under the OptFlux software platform with the aim of predicting high succinate flux. The results indicated that the mutant model lacking the glpC/b2243 gene will demonstrate increased succinate flux that is 30% higher than its wild-type control model. We can hypothesize that an additional NADH molecule was generated following the deletion of the gene and/or the alternatively preferred GldA-DhaKLM fermentative route for glycerol metabolism in E. coli may have been activated. Although the exact metabolic mechanism involved in increasing the succinate flux still remains obscure; the current study informs other studies that a model-driven, metabolic glpC/b2243 gene knockout could be applicable in filling our knowledge gap using a comprehensive experimental inquiry in the future; leading to a better understanding of the underlying metabolic function of this gene in relation to succinate production in E. coli from glycerol.
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