Artificial neural network-genetic algorithm (ANN-GA) based medium optimization for the production of human interferon gamma (hIFN-γ) in Kluyveromyces lactis cell factory

In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis). In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN-gamma production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN-gamma concentration at 50 g L-1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4-parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R-2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F-test and Akaike information criterion (AIC). The Plackett-Burman multifactorial design identified sorbitol, glycine, Na2HPO4, and MgSO4.7H(2)O as the parameters significantly influencing the hIFN-gamma production. Further, the Box-Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN-GA) was employed for the precise optimization of medium components. With ANN-GA a maximum hIFN-gamma yield of 2.1 +/- 0.3 mg L-1 in shake flask level and 3.5 +/- 0.1 mg L-1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.