0001) and four interaction terms were significant. ANOVA was used to analyze the responses under different combinations as defined by the design (Table 2). The application of RSM gave rise to the regression Equation (2) for CX production. The quadratic equation specifies an empirical relationship between CX yield and the test variables. (2) The ANOVA regression model demonstrated an adjusted coefficient of determination (R 2 adjusted ) of 0.9945, indicating 99.45% variability in the response could be explained by this model. A very low value of coefficient of variation (C.V., 0.72%) indicates better precision and reliability of the executed experiments.
P505-15 clinical trial An acceptable precision value of 64.594 was obtained as a measure of the signal-to-noise ratio, with a ratio >3.6 deemed desirable [60–62]. In this case, higher ratio indicates an adequate signal, and also proves that model can be used to navigate the design space . Table 2
shows the linear effects of D-glucose NVP-BSK805 order content and Mg2+ concentration were significant (p <0.0001) on the CX produced by D. natronolimnaea svgcc1.2736 mutants, whereas mannose content was significant. The quadratic effects of mannose content and Mg2+ concentration were significant at the 0.002% level. In Table 2 depicts an interaction between D-glucose and mannose content was not significant. These observations were also substantiated by a highly significant (p <0.001) interactive effect between the see more Pyruvate dehydrogenase variables on biomass production.
The 3D response surface plots and two dimensional contour plots were used to understand the interaction effects of medium components and optimum concentration of each component required for maximum CX production. In each set, two variables varied within their experimental range, while the other two variables remained constant at zero level. This reveals that variation in the CX value could be explained as a nonlinear function of the D-glucose and mannose content. The most significant (p <0.001) effect on CX was shown to be the linear effect of Mg2+ concentration, followed by the linear effect of D-glucose content and the quadratic effect of Mg2+ concentration, as presented in Table 2. The concentration of Mg2+ can therefore significantly influence the production and accumulation of biomass . Mg2+ acts as a stimulant by affecting the growth and activity of the microorganism, which in turn leads to a significant improvement in microbial biomass and production of CX . Figure 4A shows the response surface contour plot and 3D plots for the interactive effect of D-glucose and mannose on CX production. It was observed that mutants of D. natronolimnaea svgcc1.2736 grown in D-glucose medium and supplemented with 13.5 g L-1 mannose showed an increase in CX (7.65 mg L-1). However, CX concentration significantly decreased upon further increases in mannose content. This was likely due to inhibition facilitated by sugar concentrations higher than 13.5 g L-1.