Response surface methodology-artificial neural network-based optimization and strain improvement of cellulase production by streptomyces sp.

Abstract

Thirty-seven different colonies were isolated from decomposing logs of textile industries. From among these, a thermotolerant, gram-positive, filamentous soil bacteria Streptomyces durhamensis vs15 was selected and screened for cellulase production. The strain showed clear zone formation on the CMC agar plate after Gram’s iodine staining. Streptomyces durhamensis vs15 was further confirmed for cellulase production by estimating the reducing sugars through the dinitrosalicylic acid (DNS) method. The activity was enhanced by sequential mutagenesis using three mutagens of ultraviolet irradiation (UV), N methyl-N’-nitro-N-nitrosoguanidine (NTG), and Ethyl methanesulfonate (EMS). After mutagenesis, the cellulase activity of GC23 (mutant) was improved to 1.86-fold compared to the wild strain (vs15). Optimal conditions for the production of cellulase by the GC 23 strain were evaluated using Response Surface Methodology (RSM) and Artificial Neural Network (ANN). The effects of pH, temperature, duration of incubation, and substrate concentration on cellulase production were evaluated. Optimal conditions for the production of cellulase enzyme using Carboxymethyl cellulose as a substrate are 55 ºC of temperature, pH of 5.0, and incubation for 40 h. The cellulase activity of the mutant Streptomyces durhamensis GC23 was further optimized to 2-fold of the activity of the wild type by RSM and ANN.