Preprint Article Version 1 This version is not peer-reviewed

Genetic Optimization Algorithm for Metabolic Engineering Revisited

Version 1 : Received: 27 March 2018 / Approved: 27 March 2018 / Online: 27 March 2018 (05:55:32 CEST)

A peer-reviewed article of this Preprint also exists.

Alter, T.B.; Blank, L.M.; Ebert, B.E. Genetic Optimization Algorithm for Metabolic Engineering Revisited. Metabolites 2018, 8, 33. Alter, T.B.; Blank, L.M.; Ebert, B.E. Genetic Optimization Algorithm for Metabolic Engineering Revisited. Metabolites 2018, 8, 33.

Journal reference: Metabolites 2018, 8, 33
DOI: 10.3390/metabo8020033

Abstract

To date, several independent methods and algorithms exist exploiting constraint-based stoichiometric models to find metabolic engineering strategies that optimize microbial production performance. Optimization procedures based on metaheuristics facilitate a straightforward adaption and expansion of engineering objectives as well as fitness functions, while being particularly suited for solving problems of high complexity. With the increasing interest in multi-scale models and a need for solving advanced engineering problems, we strive to advance genetic algorithms, which stand out due to their intuitive optimization principles and proven usefulness in this field of research. A drawback of genetic algorithms is that premature convergence to sub-optimal solutions easily occurs if the optimization parameters are not adapted to the specific problem. Here, we conducted comprehensive parameter sensitivity analyses to study their impact on finding optimal strain designs. We further demonstrate the capability of genetic algorithms to simultaneously handle (i) multiple, non-linear engineering objectives, (ii) the identification of gene target-sets according to logical gene-protein-reaction associations, (iii) minimization of the number of network perturbations, and (iv) the insertion of non-native reactions, while employing genome-scale metabolic models. This framework adds a level of sophistication in terms of strain design robustness, which is exemplarily tested on succinate overproduction in Escherichia coli.

Subject Areas

metabolic strain design; heuristic optimization; constraint-based modeling

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