Modelling and optimization of fermentation factors for enhancement of alkaline protease production by isolated Bacillus circulans using feed-forward neural network and genetic algorithm
2008
Subba Rao, Ch | Sathish, T. | Mahalaxmi, M. | Suvarna Laxmi, G. | Sreenivas Rao, R. | Prakasham, R.S.
Modelling and optimization of fermentation factors and evaluation for enhanced alkaline protease production by Bacillus circulans. A hybrid system of feed-forward neural network (FFNN) and genetic algorithm (GA) was used to optimize the fermentation conditions to enhance the alkaline protease production by B. circulans. Different microbial metabolism regulating fermentation factors (incubation temperature, medium pH, inoculum level, medium volume, carbon and nitrogen sources) were used to construct a '6-13-1' topology of the FFNN for identifying the nonlinear relationship between fermentation factors and enzyme yield. FFNN predicted values were further optimized for alkaline protease production using GA. The overall mean absolute predictive error and the mean square errors were observed to be 0·0048, 27·9, 0·001128 and 22·45 U ml⁻¹ for training and testing, respectively. The goodness of the neural network prediction (coefficient of R²) was found to be 0·9993. Four different optimum fermentation conditions revealed maximum enzyme production out of 500 simulated data. Concentration-dependent carbon and nitrogen sources, showed major impact on bacterial metabolism mediated alkaline protease production. Improved enzyme yield could be achieved by this microbial strain in wide nutrient concentration range and each selected factor concentration depends on rest of the factors concentration. The usage of FFNN-GA hybrid methodology has resulted in a significant improvement (>2·5-fold) in the alkaline protease yield. The present study helps to optimize enzyme production and its regulation pattern by combinatorial influence of different fermentation factors. Further, the information obtained in this study signifies its importance during scale-up studies.
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