Application Of Response Surface Methodology On Biological Process Optimization

As an important subject in the statistical design of experiments, the Response Surface Methodology (RSM) is a collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several variables and the objective is to optimize this response. RSM is widely applied in the optimization of in the biological and chemical fields, and in food and other engineering sciences. According to the principle of the response surface analysis, the Methodology of experimental design, data handling and application of RSM are discussed in this paper. And in this study, the Response Surface Methodolog was employed in optimization two different biological processes. In order to improve the omethoate degrading by Pseudomonas MAP-3and the production of α-amylase, the Central Composite Design and Box-Behnken design were applied to optimize the degrading and the fermentation media, respectively.In order to improve the omethoate degrading by MAP-3, RSM based on a five-level four-factor Central Composite Design of experiments was used to optimize the omethoate degrading optimal levels of four important factors. The significant of the model, the significance of experimental factors, the interactions between factors and test the significance of the reliability of the test were h test by analysis of variance (ANOVA). The best omethoate degrading conditions and maximum omethoate degradation rate were found by RSM. The experimental yield of the omethoate degrading rate was in excellent agreement with the predicted omethoate degrading rate by RSM, and it markedly improved after optimization.In order to improve the production of α-amylase by fed-batch culture, the Plackett-Burman and Box-Behnken design were applied to optimize the fermentation media. Firstly, three factors (soybean power, pH and FeSO4·7H2O), which have significant effects on α-amylase production, were selected from seven variables by Plackett-Burman design. For the three significant factors, a three-level three-factor Box-Behnken design experiment with17runs was employed, and a quadratic regression model were fitting by RSM. The significant of the model, the significance of experimental factors, the interactions between factors and test the significance of the reliability of the test were h test by analysis of variance (ANOVA). The optimal fermentation medium for optimal fermentation of Aspergillus oryzae production α-amylase were found by RSM, and the predicted values by RSM was in excellent agreement with t the experimental value, it illustrated that the model can predict the actual α-amylase expression.