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Metabolites 2018, 8(2), 33; https://doi.org/10.3390/metabo8020033

Genetic Optimization Algorithm for Metabolic Engineering Revisited

Institute of Applied Microbiology—iAMB, Aachen Biology and Biotechnology—ABBt, RWTH Aachen University, Worringerweg 1, 52074 Aachen, Germany
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Received: 27 March 2018 / Revised: 20 April 2018 / Accepted: 14 May 2018 / Published: 16 May 2018
(This article belongs to the Special Issue Metabolism and Systems Biology Volume 2)
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Abstract

To date, several independent methods and algorithms exist for 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 the 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. View Full-Text
Keywords: metabolic strain design; heuristic optimization; constraint-based modeling metabolic strain design; heuristic optimization; constraint-based modeling
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Supplementary materials

  • Supplementary File 1:

    ZIP-Document (ZIP, 4959 KB)

  • Externally hosted supplementary file 1
    Doi: 10.5281/zenodo.1208048
    Link: https://github.com/Spherotob/GAMO_public
    Description: Supplementary Materials: The following are available online at www.mdpi.com/link. The Genetic Algorithm for Metabolic Engineering (GAMO) framework developed and used in this work is freely available on GitHub (https://github.com/Spherotob/GAMO_public, DOI:10.5281/zenodo.1208048). Supplementary File 1: Section I.1: Determination of reference flux distributions. Section I.2: A simplified calculation of the growth-coupling strength. Section I.3: A databank model including novel network edges. Figure S1: Scheme of a wild-type yield space showing no growth-coupling and a growth-coupled strain design. Figure S2: Maximal fitness and hamming distance progressions of GA runs for the mutation rate sensitivity analysis using a selection rate of 0.25. Figure S3: Maximal fitness and hamming distance progressions of GA runs for the mutation rate sensitivity analysis using a selection rate of 0.75. Figure S4: Maximal fitness and hamming distance progressions of GA runs for the mutation rate sensitivity analysis using a population size of 10. Figure S5: Maximal fitness and hamming distance progressions of GA runs for the mutation rate sensitivity analysis using a population size of 50. Figure S6: Maximal fitness and Hamming distance progressions applying an adaptive mutation probability approach for a basic genetic algorithm. Figure S7: Total number of fitness function evaluations after 900 generations using a basic genetic algorithm and applying different selection rates and population sizes. Figure S8: Hamming distance progressions for GA runs using selection rates between 0.15 and 0.75. Figure S9: Maximal fitness progressions applying different numbers of Gene-Flow-Events at constant numbers of total generations. Figure S10: Maximal fitness and Hamming distance progressions using a basic genetic algorithm and the E. coli core model to identify reaction deletions for succinate, ethanol, lactate, and glutamate overproduction. Figure S11: Maximal fitness and Hamming distance progressions using a basic genetic algorithm and the E. coli core model to identify gene deletions for succinate, ethanol, lactate, and glutamate overproduction. Figure S12: Maximal fitness and Hamming distance progressions of genetic algorithm runs using a minimization of intervention set size approach. Figure S13: Hamming distance progressions for genetic algorithm runs using multiple objective functions simultaneously. Figure S14: Maximal fitness and Hamming distance progressions for genetic algorithm runs using the E. coli genome-scale model iJO1366. Table S1: Minimally and maximally expected as well as standard intracellular concentrations of gaseous metabolites. Supplementary File 2: Non-native network edges for the E. coli iAF1260 core model identified following the descriptions in the Supplementary text. Supplementary File 3: Non-native network edges for the E. coli iJO1366 genome-scale model identified following the descriptions in the Supplementary text. Supplementary File 4: Genetic algorithm parameter sets used in each conducted simulation in this work. Supplementary File 5: Collection of all relevant, identified strain designs.
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Alter, T.B.; Blank, L.M.; Ebert, B.E. Genetic Optimization Algorithm for Metabolic Engineering Revisited. Metabolites 2018, 8, 33.

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