STATISTICAL MODELING AND OPTIMIZATION OF ALKALINE PROTEASE PRODUCTION FROM A NEWLY ISOLATED ALKALOPHILICBacillusSPECIES BGS USING RESPONSE SURFACE METHODOLOGY AND GENETIC ALGORITHM

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Abstract
A new hyperactive alkalophilic bacterial strain (Bacillus sp. BGS) was isolated from samples collected from soil that received the effluent of a milk processing industry located in Madurai, Tamilnadu, India, and this bacterial strain was used for the production of alkaline protease. Four out of eight variables, such as molasses, peptone, pH, and inoculum size, have been identified through Plackett–Burman (PB) design and used for the alkaline protease production. These significant variables were further optimized through a hybrid system of response surface methodology (RSM) followed by genetic algorithm (GA). The optimal combination of media components and culture conditions for maximal protease production was found to be 16.827 g/L of peptone, 1.128% (v/v) of molasses, pH value of 11, and 2% (v/v) of inoculum size. A 6.36-fold increase in protease production was achieved through the RSM–GA hybrid system. The protease activity increased significantly with an optimized medium (2,992.75 U/mL) as opposed to an unoptimized basal medium (470.35 U/mL).

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