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).