Full length articleImprovement of ε-polylysine production by marine bacterium Bacillus licheniformis using artificial neural network modeling and particle swarm optimization technique

•An advanced modeling and optimization technique for improving ε-polylysine production.•The critical nutrients affecting ε-polylysine yield are glucose and yeast extract.•The ANN topology of 4-10-1 was found to be optimum with feed-forward back propagation algorithm.•ε-polylysine production was improved by 196.7%, as compared to un-optimized medium.

ε-Polylysine is water soluble, biodegradable, edible and non-toxic homopolymer of l-lysine linked by the peptide bond between the carboxyl and ε-amino groups. ε-polylysine and its derivatives being used for past few decades for a broad range of industrial applications. However, the yields of ε-polylysine using wild type strains are comparatively low with respect to what desired for industrial production. Hence, in this study, an advanced modeling and optimization technique was applied to optimize medium parameters for enhanced ε-polylysine production by marine bacterium Bacillus licheniformis. The critical nutrients including glucose, yeast extract, magnesium sulphate and ferrous sulphate were incorporated in artificial neural networks (ANN) as input variables and ε-polylysine as the output variable. The ANN topology of 4-10-1 was found to be optimum upon training the model with feed-forward back propagation algorithm and on application of the developed model to particle swarm optimization (PSO) resulted in 3.56 ± 0.16 g L−1 of ε-polylysine under the following optimal conditions: glucose, 34 g L−1; yeast extract, 2.3 g L−1; magnesium sulphate, 0.44 g L−1 and ferrous sulphate, 0.08 g L−1. Thus, this optimization technique could significantly improve ε-polylysine by 196.7%, as compared to un-optimized medium. The potential significance of this study lies in the development of a suitable production medium for improved ε-polylysine production by an advanced optimization approach, ANN-PSO.