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Response surface methodology (RSM) is a compilation of statistical and mathematical techniques widely used to determine the effects of several experimental parameters and to optimize various biotechnological processes. This technique gives contours plots from linear, interaction and quadratic effects of two or more parameters in order to calculate the optimal response of the system. This technique has been proposed to investigate the optimization of physiochemical parameters of biotechnological fermentations using various microorganisms. RSM explores the relationships between several explanatory variables (independent variables) and one or more response variables (dependent variable). The method was introduced in 1951. The main idea of RSM is to use a sequence of designed experiments to obtain an optimal response.A second-degree polynomial model is usually used to do this.

An easy way to estimate a first-degree polynomial model is to use a factorial experiment or a fractional factorial designs. This is sufficient to determine which explanatory variables have an impact on the response variable(s) affecting the finale response of a process. Once it is suspected that only significant explanatory variables are left, then a more complicated design, such as a central composite design can be implemented to estimate a second-degree polynomial model. Such second-degree model can be used to optimize (maximize, minimize, or attain a specific target for) such final response.

Into the framework of developing novel processes aimed at producing novel molecules by yeasts, DBVPG Collection currently uses RSM to find and validate the better combination of selected variables, which optimize the selected responses.

 

               

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