2.2. Optimizing BioLector fermentation screening through central composite design of response surface methodology
The DoE packagefacilitated the generation of 30 experiments using the RSM to optimize carefully selected model terms, encompassing MgSO4, glycerol, glucose, and certain LB broth concentration (referred as TB). The experimental runs were conducted utilizing the BioLector microfermenter in a 1 mL screening culture that maintained a constant concentration of 10 mM KCI, 15 mM MgCI2, 15 mM KH2PO4, 10 mM thiamine, and 50 g/L 2X LB Miller media (Supplementary Table. 2 Fig. 5), complemented by 0.25 mM IPTG tailored to each specific design point. Once conducting analyses using various models, the quadratic model emerged as a suitable candidate for predicting and validating optimal conditions. Notably, the model exhibited a significant p-value of 0.0037, thereby providing a reliable prediction framework. Additionally, the model’s lack of fit displayed insignificance at 0.4479 relative to the pure error, suggesting the error’s immateriality in influencing the accuracy of the proposed model (Table 2). The model’s R2 value of 0.6866 indicated that it reasonably fits the experimental data. Furthermore, the adequate precision value of 7.861 indicates the sufficiency of the signal for the biological system under scrutiny. The perturbation analysis, factorial contour plots and 3D response surface of the central composite design was operated to gain a deeper understanding of the impact of independent variables’ major effects and interactions on insulin yield (Fig. 6, Fig. 7, and Fig. 8). Perturbation of CCD analysis specifically clarified the primary marks of variables A, B, C, and D, on insulin yield, as depicted in Fig. 6. The two-dimensional contour and three-dimensional response surface plot effectively portrayed the intricate relationship between the independent variables and the ensuing insulin yield (Fig. 7, Fig.8). These independent variables were deliberately manipulated across a predefined range to systematically investigate their combined influence on insulin production. The contour plots (Fig.7), in particular, offer invaluable insights into the inherent characteristics and magnitude of the interactions among various contributing factors. Notably, when glycerol levels were elevated, there was a substantial reduction in the resultant insulin yield, plummeting from 12.6259 mg/ml to 7.25534 mg/ml. Likewise, the simultaneous increase of glycerol and media content to elevated levels (exceeding 30 g/l) resulted in a decrease in insulin yield from 11.124 mg/ml to 6.84772 mg/ml. Conversely, the presence or absence of MgSO4exhibited no discernible impact on the insulin yield of significance. The investigation of the interplay between the dependent and independent variables was further elucidated by implementing 3D response surface plots (Fig. 8). These plots effectively affirmed the intricate and interactive influence of specific glucose, glycerol, MgSO4 concentrations, and media content on insulin yield. Notably, when considering the actual factors of LB (30 g/l) and MgSO4 (5mM), it was regarded that a lower concentration of glycerol and the highest concentration of glucose displayed a positive correlation with the production of insulin at higher levels compared to the other variables. Consequently, based on these findings, four distinct scenarios were statistically derived and subsequently subjected to experimental validation. The experimentation was achieved using the BioLector micro-fermenter, with each scenario being replicated three times to ensure the reliability and accuracy of the results (Table 3). Contempt the absence of substantial discrepancies among the scenarios, it is noteworthy that scenario III, characterized by a glucose concentration of 8.78 mM, glycerol concentration of 10 g/L, LB of 30 g/l, and MgSO4 concentration of 15 mM, exhibits the slightest deviation and most heightened insulin product in comparison to the additional scenarios (Table 3).