Abstract
The applicability of the statistical tools coupled with artificial intelligence techniques was tested to optimize the critical medium components for the production of extracellular cholesterol oxidase (COD; an enzyme of commercial interest) from Streptomyces rimosus MTCC 10792. The initial medium component screening was performed using Placket-Burman design with yeast extract, dextrose, starch and ammonium carbonate as significant factors. Response surface methodology (RSM) was attempted to develop a statistical model with a significant coefficient of determination (R2 = 0.89847), followed by model optimization using Genetic Algorithm (GA). RSM-GA based optimization approach predicted that the combination of yeast extract, dextrose, starch and ammonium carbonate at concentrations 0.99, 0.8, 0.1, and 0.05 g/100 ml respectively, has resulted in 3.6 folds increase in COD production (5.41 U/ml) in comparison with the un-optimized medium (1.5 U/ml). COD was purified 10.34 folds having specific activity of 12.37 U/mg with molecular mass of 54 kDa. The enzyme was stable at pH 7.0 and 40 °C temperature. The apparent Michaelis constant (Km) and Vmax values of COD were 0.043 mM and 2.21 μmol/min/mg, respectively. This is the first communication reporting RSM-GA based medium optimization, purification and characterization of COD by S. rimosus isolated from the forest soil of eastern India.
Highlights
The applicability of the statistical tools coupled with artificial intelligence techniques was tested to optimize the critical medium components for the production of extracellular cholesterol oxidase (COD; an enzyme of commercial interest) from Streptomyces rimosus Microbial Type Culture Collection (MTCC) 10792
Attempts have been made by employing Response surface methodology (RSM) coupled Genetic Algorithm (GA) approach to quantitatively evaluate the individual and combined interaction effect(s) of the medium components on COD production from Streptomyces rimosus isolated from the eastern part (Chhatisgarh) of India, along with the purification and characterization of the said enzyme
The isolate, S. rimosus MTCC 10792 was capable of changing the color of the medium into intense brown
Summary
The applicability of the statistical tools coupled with artificial intelligence techniques was tested to optimize the critical medium components for the production of extracellular cholesterol oxidase (COD; an enzyme of commercial interest) from Streptomyces rimosus MTCC 10792. The conventional non-statistical one-factor-at-a-time (OFAT) approach is excessively time-consuming, lacks precision in identifying the critical factors that influence the production of desired metabolite(s), and fails to elucidate the interactions among the factors being studied. The conventional non-statistical one-factor-at-a-time (OFAT) approach is excessively time-consuming, lacks precision in identifying the critical factors that influence the production of desired metabolite(s), and fails to elucidate the interactions among the factors being studied18 To overcome these drawbacks, various statistical methods have efficiently been used for the optimization of the medium composition. Attempts have been made by employing RSM coupled GA approach to quantitatively evaluate the individual and combined interaction effect(s) of the medium components on COD production from Streptomyces rimosus isolated from the eastern part (Chhatisgarh) of India, along with the purification and characterization of the said enzyme
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