Albany 2015:Book of Abstracts
June 9-13 2015
©Adenine Press (2012)
Model-aided anaerobic metabolic gene knockout of malate dehydrogenase (mdh) gene predicts increased succinate production in Escherichia coli
Succinic acid is considers to be a platform chemical with divergent applications as a precursor for syntheses of commodity and specialty chemicals. Its biobased production could be a green technology when produced by microbial fermentation using Escherichia coli as a chassis host. Metabolic engineering for increased production of succinic acid using glucose as substrate is beset with limitation of NADH availability under anaerobic conditions in E. coli. In the present work, we used the most recent genome scale metabolic model of E. coli iJ01366 (Orth et al. 2011) to predict metabolic gene knockout of mdh/b3236 with the aim of increasing NADH availability using the glucose substrate. The Minimization of Metabolic Adjustment (MOMA) (Segre et al. 2002) under the OptFlux software platform (Rocha et al. 2010) is used as the simulation algorithm. The results indicates that the mutant model produced 185% high succinate production compared to its parent model, indicating a nearly two fold increase in succinate production. The results further demonstrates that an additional molecule of NADH could have been generated by the deletion of the mdh/b3236 gene. These findings open-up a novel and/or comprehensive model-aided discovery on the metabolic mechanism of NADH regeneration in relation to succinate production in E. coli after the mdh/b3236 gene knockout.
Orth J.D., Conrad T.M., Na J., Lerman J.A., Nam H., Feist A.M. & Palsson B.O. (2011) A comprehensive genome-scale reconstruction of Escherichia coli metabolism--2011. Mol Syst Biol 7, 535.
Rocha I., Maia P., Evangelista P., Vilaca P., Soares S., Pinto J.P., Nielsen J., Patil K.R., Ferreira E.C. & Rocha M. (2010) OptFlux: an open-source software platform for in silico metabolic engineering. BMC Syst Biol 4, 45.
Segre D., Vitkup D. & Church G.M. (2002) Analysis of optimality in natural and perturbed metabolic networks. Proc Natl Acad Sci U S A 99, 15112-7.
Bashir Sajo Mienda
Bioinformatics Research Grouop (BIRG)