Optimization of Bacterial Strains with Variable-Sized Evolutionary Algorithms

In metabolic engineering it is difficult to identify which set of genetic manipulations will result in a microbial strain that achieves a desired production goal, due to the complexity of the metabolic and regulatory cellular networks and to the lack of appropriate modeling and optimization tools. In this work, evolutionary algorithms (EAs) are proposed for the optimization of the set of gene deletions to apply to a microorganism, in order to maximize a given objective function. Each mutant strain is evaluated by resorting to the simulation of its phenotype using the flux-balance analysis approach, together with the premise that microorganisms have maximized their growth along natural evolution. A new set based representation is used in the EAs, using variable size chromosomes, allowing for the automatic discovery of the optimal number of gene deletions. This approach was compared with a traditional binary-based genetic algorithm. Two case studies are presented considering the production of succinic and lactic acid as the target, with the bacterium E. coli. The variable size EAs, outperformed the other approaches tested, allowing to reach good results regarding the production of the desired compounds, and additionally presenting low variability among the several runs