In silico Prediction of Gene Deletion Targets in Escherichia coli for Enhanced Succinic Acid Production Using a Model-Guided Approach Under the Optflux Software Platform

In silico prediction of gene deletion targets in Escherichia coli for enhanced succinic acid production using a model-guided approach under the OptFlux software platform

Bashir Sajo Mienda and Mohd Shahir Shamsir

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

Abstracts

The advent and proliferation of Escherichia coli genome scale metabolic models coupled with breakthroughs in computational tools, is revolutionizing the field of metabolic engineering and synthetic biology. E. coli has been metabolically engineered using a number of established experimental trial and error strategies, to enhance the production of succinic acid on glucose and glycerol substrates under anaerobic conditions. However, investigation of in silico prediction of gene knockout targets for increased succinic acid production, using a model-guided approach under the OptFlux software platform, is yet to be elucidated. Here, we show using the most recent metabolic reconstruction of E. coli iJ01366 model that in silico deletion of pyruvate formate lyase A (pflA/b0902) and lactate dehydrogenase A (ldhA/b1380) using the OptFlux software platform has been predicted to enhance succinic acid production using glucose and glycerol as solitary carbon sources. The mutant models constructed in this study exhibited higher succinate production, of 115% and 121% respectively, when compared to their parent models on glucose and glycerol. Accordingly, growth rates of the mutant models were maintained at 60% on glucose and 67% on glycerol compared to their respective wild-type models. We hypothesized that a significant succinate flux could be achieved by knocking out pflA/b0902 and ldhA/b1380 in E. coli using glycerol and glucose as substrates, and that the choice of substrate is critical to enhancing succinic acid production. The results further demonstrates that the OptFlux software using minimization of metabolic adjustment (MOMA) as the simulation algorithm holds great promise as a reference computational tool for in silico metabolic engineering applications, that can guide future experimental studies, which is not only limited to succinic acid production in E. coli, but also for other important chemical compounds produced by microbial cell factories.

Key words: In silico prediction, gene deletion, Escherichia coli, Model-guided approach, OptFlux succinic acid.