PLoS ONESystems BiologyPeer-reviewed journal article
OptEnvelope: a target point guided method for growth-coupled production using knockouts
Ehsan MotamedianiD, Kristaps BerzinsiD, Reinis MuiznieksiD, Egils StalidzānsiD
Abstract
Finding the best knockout strategy for coupling biomass growth and production of a target metabolite using a metabolic model is a challenge in biotechnology. In this research, a three-step method named OptEnvelope is developed based on finding minimal active reactions for a target point in the feasible solution space using a mixed-integer linear programming formula. The method initially finds the reduced desirable solution space (envelope) in the product versus biomass plot by removing all inactive reactions. Then, with reinsertion of the deleted reactions, OptEnvelope attempts to reduce the number of knockouts so that the desirable envelope is preserved. Additionally, OptEnvelope searches for envelopes with higher minimum production rates or fewer knockouts by evaluating different target points within the desired solution space. It is possible to limit the maximal number of knockouts. The method was implemented on metabolic models of E. coli and S. cerevisiae to test the method benchmarking the capability of these industrial microbes for overproduction of acetate and glycerol under aerobic conditions and succinate and ethanol under anaerobic conditions. The results indicate that E. coli is more appropriate to produce acetate and succinate while S. cerevisiae is a better host for glycerol production. Gene deletions for some of the proposed reaction knockouts have been previously reported to increase the production of these metabolites in experiments. Both organisms are suitable for ethanol production, however, more knockouts for the adaptation of E. coli are required. OptEnvelope is available at https://github.com/lv-csbg/optEnvelope.
- organism: E. coli
- organism: yeast
- assay: metabolomics
- research focus: variant calling
- approach: mixed model
- Citations
- 10
- FWCI
- 0.89
- Field percentile
- top 30%
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