Abstract
Background:Streptomyces olivaceusMTCC 6820 is a potent microorganism for cholesterol oxidase (ChOx) production through the submerged fermentation process. Statistical optimization of the process parameters for submerged fermentation enhances the production of enzymes.Objective:This work is aimed to optimize the culture conditions for the fermentative production of cholesterol oxidase byStreptomyces olivaceusMTCC 6820 using combined Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques.Methods:The ChOx production (U/ml) was modeled and optimized as a function of six independent variables (culture conditions) using RSM and ANN.Results:ChOx production enhanced 2.2 fold,i.e1.9 ± 0.21 U/ml under unoptimized conditions to 4.2 ± 0.51 U/ml after the optimization of culture conditions. Higher coefficient of determination (R2= 97.09 %) for RSM and lower values of MSE (0.039) and MAPE (3.46 %) for ANN proved the adequacy of both the models. The optimized culture conditions predicted by RSMvs. ANN were pH (7.5), inoculum age (48 h), inoculum size (11.25 % v/v), fermentation period (72 h), incubation temperature (30°C) and shaking speed (175 rpm).Conclusion:The modeling, optimization and prediction abilities of both RSM and ANN methodologies were compared. The values of Pearson correlation coefficient (r) (ANN0.98> RSM0.95), regression coefficient (R2) between experimental activity, RSM and ANN predicted ChOx activity, respectively (ANN0.96> RSM0.90) and Absolute Average Deviation (AAD) for (ANN3.46%< RSM9.87%) substantiated better prediction ability of ANN than RSM. These statistical values indicated the superiority of ANN in capturing the non-linear behavior of the system.
Highlights
Cholesterol oxidase (EC 1.1.3.6) is a bi-functional (FlavinAdenine Dinucleotide) FAD-dependent enzyme
Response Surface Methodology (RSM) was performed to define the interactive effects of the culture conditions on cholesterol oxidase (ChOx) activity as well as to maximize its production
The experimental values of ChOx were fitted to the quadratic equation (Eq 1), and the following second-order polynomial regression equation (Eq 8) in coded units was obtained: Y = -11.55 + 2.494 X1 + 0.0123 X2 + 0.4544 X3 + 0.05829 X4 + 0.1743 X5 - 0.00561 X6 (8)
Summary
Cholesterol oxidase (EC 1.1.3.6) is a bi-functional (FlavinAdenine Dinucleotide) FAD-dependent enzyme. ChOx has been predominantly used for the development and fabrication of different types of biosensors /nanobiosensors for monitoring serum cholesterol detection [11]. Despite their widespread potential applications, the commercial production of ChOx is still a challenging aspect, due to its low yield through fermentation process [12, 13]. Streptomyces olivaceus MTCC 6820 is a potent microorganism for cholesterol oxidase (ChOx) production through the submerged fermentation process. Statistical optimization of the process parameters for submerged fermentation enhances the production of enzymes
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