Enhanced integrated reliability-based algorithm for identifying the optimal gene knockout in strain optimization

Abstract

This study developed an enhanced reliability-based integrating (RBI) algorithm by incorporating metaheuristic optimization algorithms into the RBI framework in order to identify optimal gene knockout for strain optimization. To enhance the RBI algorithm, nine metaheuristic optimizations were used: aquila optimization (AO), differential search algorithm (DSA), genetic algorithm (GA), genetic algorithm based on natural selection theory (GABONST), grey wolf optimizer (GWO), komodo mlipir algorithm (KMA), particle swarm optimization (PSO), simulated annealing (SA), and whale optimization algorithm (WOA). The algorithms were simulated with six microbial strain models to optimize the production of succinate and ethanol under aerobic and anaerobic conditions. The analysis indicated that the enhanced algorithms have effectively identified the optimal gene knockout. Furthermore, the three most effective algorithms identified were WOARBI, GWORBI, and GABONSTRBI, which produced optimal mutant strains with the highest succinate or ethanol production rates. This study’s results demonstrated that the metaheuristic optimization algorithms effectively improved the performance of the RBI algorithm.

Keywords

In silico / Metabolic engineering / Strain optimization / Metaheuristic / Gene regulatory networks / Metabolic networks

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