In silico evaluation of the effect of PFL gene knockout on the production of D-lactate by Escherichia coli genome scale model using the OptFlux software platform

The increase availability of genome scale metabolic models of Escherichia coli and computational successes is revolutionizing the field of metabolic engineering and synthetic microbiology. E. coli has been experimentally established to produce D-lactate under micro-aerobic conditions when pyruvate f...

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Bibliographic Details
Main Authors: Mienda, Bashir Sajo, Shamsir, Mohd. Shahir, Mohd. Salleh, Faezah
Format: Article
Published: Indian Society for Education and Environment 2015
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Online Access:http://eprints.utm.my/id/eprint/55767/
http://www.indjst.org/index.php/indjst/article/viewFile/56501/44129
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Summary:The increase availability of genome scale metabolic models of Escherichia coli and computational successes is revolutionizing the field of metabolic engineering and synthetic microbiology. E. coli has been experimentally established to produce D-lactate under micro-aerobic conditions when pyruvate formate lyase (PFL) genes are knocked out. However, investigation on the in silico prediction and for evaluation of the effect of PFL genes knockout on the production of D-lactate using E. coli genome scale metabolic model with regulatory on/off minimization (ROOM) under the OptFlux software platform remained under explored. Here, we demonstrate that metabolic engineering strategies using the OptFlux software platform by gene knockout simulation of pflA/b0902, pflB/b0903, pflC/b3952 and pflD/b3951 have been predicted to increase D-lactate production in E. coli and hence maintaining a growth rate that is 96% of the wild-type model. The deletion of the PFL genes have been established to increase D-lactate production in E. coli. The results obtained in this study is in agreement with the previously established experimental studies. These findings suggests that the OptFlux software platform using ROOM as the simulation algorithm, can prospectively and effectively predict future metabolic engineering targets for increased D-lactate production in E. coli and/or other microbial chemical syntheses.