Application of response surface Methodology coupled with Artificial Neural network and genetic algorithm to model and optimize symbiotic interactions between Chlorella vulgaris and Stutzerimonas stutzeri strain J3BG for chlorophyll accumulation

Abstract

Research on microalgae has surged due to its diverse biotechnological applications and capacity for accumulating bioactive compounds. Despite considerable advancements, microalgal cultivation remains costly, prompting efforts to reduce expenses while enhancing productivity. This study proposes a cost-effective approach through the coculture of microalgae and bacteria, exploiting mutualistic interactions. An engineered consortium of Chlorella vulgaris and Stutzerimonas stutzeri strain J3BG demonstrated biofilm-like arrangements, indicative of direct cell-to-cell interactions and metabolite exchange. Strain J3BG's enzymatic characterization revealed amylase, lipase, and protease production, sustaining mutual growth. Employing Response Surface Methodology (RSM), Artificial Neural Network (ANN), and Genetic Algorithm (GA) in a hybrid modeling approach resulted in a 2.1-fold increase in chlorophyll production. Optimized conditions included a NaNO3 concentration of 128.52 mg/l, a 1:2 (Algae:Bacteria) ratio, a 6-day cultivation period, and a pH of 5.4, yielding 10.92 ± 0.88 mg/l chlorophyll concentration.