ArticlePloS one2015
Artificial Intelligence versus Statistical Modeling and Optimization of Cholesterol Oxidase Production by using Streptomyces Sp.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 31 citations in OpenAlex.
- Characterization of natural therapeutic compounds producing novel bacterial strains isolated from Hyderabad, India.Scientific reports · 2025Article
- Statistical and neural network modeling of β-glucanase production by Streptomyces albogriseolus (PQ002238), and immobilization on chitosan-coated magnetic microparticles.Bioresources and bioprocessing · 2025Article
- Improved production of Bacillus subtilis cholesterol oxidase by optimization of process parameters using response surface methodology.Journal, genetic engineering & biotechnology · 2023Article
- A review of algorithmic approaches for cell culture media optimization.Frontiers in bioengineering and biotechnology · 2023Review
- Enhanced Pharmaceutically Active Compounds Productivity fromMolecules (Basel, Switzerland) · 2021Article
- Ameliorating process parameters for zeaxanthin yield in Arthrobacter gandavensis MTCC 25325.AMB Express · 2020Article
- Article
- Characterization and overproduction of cell-associated cholesterol oxidase ChoD from Streptomyces lavendulae YAKB-15.Scientific reports · 2019Article
- Response Surface Methodology-Genetic Algorithm Based Medium Optimization, Purification, and Characterization of Cholesterol Oxidase from Streptomyces rimosus.Scientific reports · 2018Article
- Recent advances in culture medium design for enhanced production of monoclonal antibodies in CHO cells: A comparative study of machine learning and systems biology approaches.Biotechnology advancesReview
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8 authors at 4 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
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.
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