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Siddique, Samia,Nelofer, Rubina,Syed, Quratulain,Adnan, Ahmad,Qureshi, Fahim Ashraf 한국응용생명화학회 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.5
Avermectin is an environment friendly bio-insecticide. Optimization of the culture conditions for avermectin B1b production has not been carried out before using Artificial Neural Network (ANN) method. The present work is therefore conducted to optimize some important factors including yeast extract, $MgSO_4.7H_2O$, and temperature for the avermectin B1b production using ANN methodology from Streptomyces avermitilis DSM 41445. The optimum levels for the yeast extract, $MgSO_4.7H_2O$, and temperature were 16.0 (g/L), 5.0 (g/L) and $32^{\circ}C$ respectively. Maximum effect was observed by yeast extract. Avermectin B1b yield was increased up to 150% after optimization. ANN was found to be a powerful technique for the optimization and prediction of avermectin B1b production from Streptomyces avermitilis DSM 41445.
Siddique, Samia,Syed, Quratulain,Nelofer, Runbina,Adnan, Ahmad,Mansoor, Habiba,Qureshi, Fahim Ashraf The Korean Society for Applied Biological Chemistr 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.3
Present study was conducted to optimize avermectin B1b production from S.avermitilis 41445 UV45(m)3 using artificial neural network and response surface methodology. Three variables NaCl, KCl, and pH were used for optimization. Coefficient of determination and adjusted coefficient of determination have very poor values for RSM. Values predicted by RSM for experiments were also much different from the observed avermectin production. Comparatively predicted avermectin levels by ANN were very close to observed values with much higher $R^2$ and adjusted $R^2$. Optimum levels of NaCl, KCl, and pH predicted by ANN were 1.0 g/L, 0.5 g/L, and 7.46 respectively. Sensitivity analysis predicted highest effect being shown was by pH followed by NaCl and KCl. About 37.89 folds increase in avermectin B1b production was observed at optimum levels of three variables envisage by ANN. Optimum levels, ranking order of variables, and the predicted avermectin on the optimum levels by the RSM was much different from ANN values. Results revealed that ANN is a better optimization tool for given strain than RSM.
Samia Siddique,Quratulain Syed,Runbina Nelofer,Ahmad Adnan,Habiba Mansoor,Fahim Ashraf Qureshi 한국응용생명화학회 2014 Applied Biological Chemistry (Appl Biol Chem) Vol.57 No.3
Present study was conducted to optimize avermectinB1b production from S.avermitilis 41445 UV45(m)3 usingartificial neural network and response surface methodology. Threevariables NaCl, KCl, and pH were used for optimization. Coefficientof determination and adjusted coefficient of determination havevery poor values for RSM. Values predicted by RSM for experimentswere also much different from the observed avermectin production. Comparatively predicted avermectin levels by ANN were veryclose to observed values with much higher R2 and adjusted R2. Optimum levels of NaCl, KCl, and pH predicted by ANN were1.0 g/L, 0.5 g/L, and 7.46 respectively. Sensitivity analysis predictedhighest effect being shown was by pH followed by NaCl and KCl. About 37.89 folds increase in avermectin B1b production wasobserved at optimum levels of three variables envisage by ANN. Optimum levels, ranking order of variables, and the predictedavermectin on the optimum levels by the RSM was muchdifferent from ANN values. Results revealed that ANN is a betteroptimization tool for given strain than RSM.