In silico metabolic engineering prediction of Escherichia coli genome model for production of D-lactic acid from glycerol using the OptFlux software platform
The advent of genome scale metabolic models of Escherichia coli coupled with limited successes in computational advancement could facilitate rapid advancement in the field of metabolic engineering and synthetic biology. E. coli has been subjected to various metabolic engineering approaches using est...
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| Main Authors: | , , |
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| Format: | Article |
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Aizeon Publishers
2014
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/59792/ |
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| Summary: | The advent of genome scale metabolic models of Escherichia coli coupled with limited successes in computational advancement could facilitate rapid advancement in the field of metabolic engineering and synthetic biology. E. coli has been subjected to various metabolic engineering approaches using established experimental methods to produce D-lactate under micro-aerobic conditions using glycerol as substrate. However, investigation on the in silico prediction and/or deletion of competing pathway genes on glycerol for the production of D-lactate by E. coli genome scale model using regulatory on or off minimization (ROOM) under the OptFlux software platform is yet to be elucidated. Here, we show that in silico metabolic engineering using this software platform by simulating the knocking out of pyruvate formate lyase (pflB/b0903), fumarate reductase (frdA/b4154), phosphoacetyltransferase (pta/b2297) and alcohol/acetaldehyde dehydrogenase (adhE/b1241) have been predicted to increase D-lactate production in E. coli. The mutant models constructed in this study exhibited growth rate that is 96 % of the wild-type model, and hence maintaining a significant flux for D-lactate production. The results reported herein, were found to be in conformity with previously established experimental studies. These findings indicates that the OptFlux software platform using ROOM as simulation algorithm hold great promise as potential software platform that can accurately predict metabolic engineering targets to guide future experimental studies not only for D-lactate production in E. coli but also for other microbial chemical compounds of interests. |
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