2.4.1 Statistical approaches for optimization
To evaluate the applicability of the systemic approach for the metabolic engineering of microorganisms, the regulatory defined medium was designed for S. cerevisiae to remove intracellular constraints through recognized candidates (Ehsan Motamedian et al., 2019). Activator and inhibitor compounds of enzymes were obtained from the BRENDA database for up and down regulations, respectively and the regulatory effect of these compounds has been confirmed empirically. Each regulator was added separately to the medium, and its impact on the production of ethanol was evaluated experimentally. Moreover, DOE was used to screen and maximize the concentration of each fruitful compound that was applied. Plackett-Burman design is beneficial in screening from a long list of compounds, because of fewer required runs. This approach suggests that the main impacts will be much higher than the interactions between two factors. Hence, this methodology can be used to identify the most significant independent variables for the optimization stage. The experimental data analysis was conducted using Design Expert® software version 7.0 (STAT-EASE Inc., Minneapolis, USA). As shown in Table S3, each independent variable was evaluated at two levels, a high (+) and a low (−) level. The concentration ranges of the chosen compounds were determined by experimental studies based on the literature review. The ranges should be neither short nor wide in order to represent the effect of changing factor’s value appropriately. It is worth noting that for a proper comparison, the ethanol concentration assessments were conducted when the glucose concentration was depleted to zero.
In the present study, the significant factors identified in the Plackett-Burman experiment were employed in a full 2-level factorial design. The approach is ideally suited for considering the effects of interaction among the variables that affected the response based on the contribution percentage of the evaluated variables, and it generally works well for optimizing the process. The optimal conditions were predicted and assessed for maximum ethanol production obtained from 8 experiments using Design Expert® software version 7.0. The actual and coded values of factors are shown in Table S4. Variables that significantly affected the production of ethanol were determined using a confidence level above 95% or a p-value below 0.05. The data are statistically evaluated by analysis of variance (ANOVA).