Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.

Abstract

Cholesterol oxidase (COD) is a bi-functional FAD-containing oxidoreductase which catalyzes the oxidation of cholesterol into 4-cholesten-3-one. The wider biological functions and clinical applications of COD have urged the screening, isolation and characterization of newer microbes from diverse habitats as a source of COD and optimization and over-production of COD for various uses. The practicability of statistical/ artificial intelligence techniques, such as response surface methodology (RSM), artificial neural network (ANN) and genetic algorithm (GA) have been tested to optimize the medium composition for the production of COD from novel strain Streptomyces sp. NCIM 5500. All experiments were performed according to the five factor central composite design (CCD) and the generated data was analysed using RSM and ANN. GA was employed to optimize the models generated by RSM and ANN. Based upon the predicted COD concentration, the model developed with ANN was found to be superior to the model developed with RSM. The RSM-GA approach predicted maximum of 6.283 U/mL COD production, whereas the ANN-GA approach predicted a maximum of 9.93 U/mL COD concentration. The optimum concentrations of the medium variables predicted through ANN-GA approach were: 1.431 g/50 mL soybean, 1.389 g/50 mL maltose, 0.029 g/50 mL MgSO4, 0.45 g/50 mL NaCl and 2.235 ml/50 mL glycerol. The experimental COD concentration was concurrent with the GA predicted yield and led to 9.75 U/mL COD production, which was nearly two times higher than the yield (4.2 U/mL) obtained with the un-optimized medium. This is the very first time we are reporting the statistical versus artificial intelligence based modeling and optimization of COD production by Streptomyces sp. NCIM 5500.

Statistical optimization of medium constituents and conditions for improved antimicrobial compound production by marine Streptomyces sp. JRG-04

“…JRG-04 isolate. Similar to this study, production media optimization was also reported for a newly isolated species of Streptomyces identified to the genus level [24,25].…”

Comparison of Gaussian process regression, artificial neural network, and response surface methodology modeling approaches for predicting drying time of mosambi (Citrus limetta) peel

“…There are several studies that have compared RSM and ANN for modeling of different processes (Desai et al, ; Gomes & Awruch, ; Pathak et al, ; Youssefi, Emam‐Djomeh, & Mousavi, ). Both models were compared based on their capability of predicting both seen and unseen data.…”

Preparation and Evaluation of the ZnO NP–Ampicillin/Sulbactam Nanoantibiotic: Optimization of Formulation Variables Using RSM Coupled GA Method and Antibacterial Activities

“…The statistical/AI approaches like RSM, ANN and GA are some popular techniques used in the optimization of various parameters like metabolite production, extraction condition etc. [16,17,37]. Response surface methodology is the most commonly used statistical technique used for depicting the nature of the response within the framed design space [17], whereas the genetic algorithm mimics the biological mutation process; hence it is based upon the biological principle of "survival of the fittest".…”