Multi level statistical optimization of l-asparaginase from Bacillus subtilis VUVD001

Physical and chemical factors influencing the anti-leukemic l-asparaginase enzyme production by Bacillus subtilis VUVD001 were optimized using multi-stage optimization on the basis of preliminary experimental outcomes obtained by conventional one-factor-at-a-time approach using shake flasks. Process variables namely carbon, nitrogen sources, pH and temperature were taken into consideration during response surface methodology (RSM) optimization. The finest enzyme activity of 0.51 IUml^-1 obtained by OFAT method was enhanced by 3.2 folds using RSM optimization. Artificial neural network (ANN) modelling and genetic algorithm (GA) based optimizations were further carried out to improve the enzyme drug yield. Results were also validated by conducting experiments at optimum conditions determined by RSM and GA optimization methods. The novel bacterium yielded in 2.88 IUml^-1 of enzyme activity at optimum process variables determined by GA optimization, i.e., 0.5% glucose, 8.0% beef extract, 8.3 pH and 49.9 °C temperature. The study explored the optimized culture conditions for better yielding of anti-leukemic enzyme drug from a new bacterial source namely Bacillus subtilis VUVD001.