Genetic Optimization Algorithm for Metabolic Engineering Revisited

Metabolites
Tobias B AlterBirgitta E Ebert

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 th...Continue Reading

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Citations

Apr 30, 2019·Biotechnology and Bioengineering·Romina Lasry TestaSoledad M Diaz
Aug 30, 2019·BMC Bioinformatics·Tobias B Alter, Birgitta E Ebert
Aug 19, 2020·Metabolic Engineering·Salome C NiesBirgitta E Ebert
Nov 9, 2021·PLoS Computational Biology·Marzia Di FilippoDario Pescini

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Software Mentioned

SimOptStrain
OptStrain
gcOpt
Windows
OptGene
MiMBl
Optknock
MATLAB
GA

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