Application of response surface methodology for maximizing dextransucrase production from Leuconostoc mesenteroides NRRL B-640 in a bioreactor

Waste valorization as low-cost media engineering for auxin production from the newly isolated Streptomyces rubrogriseus AW22: Model development

Indole-3-acetic acid (IAA) represents a crucial phytohormone regulating specific tropic responses in plants and functions as a chemical signal between plant hosts and their symbionts. The Actinobacteria strain of AW22 with high IAA production ability was isolated in Algeria for the first time and was characterized as Streptomyces rubrogriseus through chemotaxonomic analysis and 16 S rDNA sequence alignment. The suitable medium for a maximum IAA yield was engineered in vitro and in silico using machine learning-assisted modeling. The primary low-cost feedstocks comprised various concentrations of spent coffee grounds (SCGs) and carob bean grounds (CBGs) extracts. Further, we combined the Box-Behnken design from response surface methodology (BBD-RSM) with artificial neural networks (ANNs) coupled with the genetic algorithm (GA). The critical process parameters screened via Plackett-Burman design (PBD) served as BBD and ANN-GA inputs, with IAA yield as the output variable. Analysis of the putative IAA using thin-layer chromatography (TLC) and (HPLC) revealed Rf values equal to 0.69 and a retention time of 3.711 min, equivalent to the authentic IAA. AW 22 achieved a maximum IAA yield of 188.290 ± 0.38 μg/mL using the process parameters generated by the ANN-GA model, consisting of L-Trp, 0.6%; SCG, 30%; T°, 25.8 °C; and pH 9, after eight days of incubation. An R² of 99.98%, adding to an MSE of 1.86 × 10^-5 at 129 epochs, postulated higher reliability of ANN-GA-approach in predicting responses, compared with BBD-RSM modeling exhibiting an R² of 76.28%. The validation experiments resulted in a 4.55-fold and 4.46-fold increase in IAA secretion, corresponding to ANN-GA and BBD-RSM models, respectively, confirming the validity of both models.

Fermentative Production of Naringinase from Aspergillus niger van Tieghem MTCC 2425 Using Citrus Wastes: Process Optimization, Partial Purification, and Characterization

Enzymatic hydrolysis of naringin by the action of naringinase is one of the standard practices adopted in the citrus fruit juice industry for debittering. In the present study, a submerged fermentation condition was optimized for producing naringinase from Aspergillus niger van Tieghem MTCC 2425. As per Placket-Burman design, pH (3-5), incubation temperature (26-30 °C), and inducer concentration (12-18 g·L^-1) were the most important factors influencing the naringinase production. Naringin from citrus waste was used as an inducer. A rotatable central composite design was employed on these three variables and the numerical optimization predicted that fermentation at 29.8 °C, pH 4.7, and inducer concentration of 14.9 g L^-1 would yield a maximum naringinase activity of 545.2 IU g^-1. During partial purification, ion exchange chromatography led to a 9.92-fold increase in enzyme activity resulting a specific activity of 5460 IU g^-1 with an activity recovery of 17%. As reflected by SDS-PAGE profile, the partially purified naringinase showed the molecular weight bands of 10-20, 65, and 80 kDa, respectively. The purified form of enzyme showed optimum stability at pH 5 and 50 °C. The naringinase activity was completely retained up to 150 days when stored at 4 °C.

Optimization of fermentation conditions through response surface methodology for enhanced antibacterial metabolite production by Streptomyces sp. 1-14 from cassava rhizosphere

Streptomyces species 1-14 isolated from cassava rhizosphere soil were evaluated for their antibacterial efficacy against Fusarium oxysporum f.sp. cubense race 4 (FOC4). Of the 63 strains tested, thirteen exhibited potent antibacterial properties and were further screened against eight fungal pathogens. The strain that showed maximum inhibition against all of the test pathogens was identified by 16S rDNA sequencing as Streptomyces sp. 1-14, was selected for further studies. Through the propagation of Streptomyces sp. 1-14 in soil under simulated conditions, we found that FOC4 did not significantly influence the multiplication and survival of Streptomyces sp. 1-14, while indigenous microorganisms in the soil did significantly influence Streptomyces sp. 1-14 populations. To achieve maximum metabolite production, the growth of Streptomyces 1-14 was optimized through response surface methodology employing Plackett-Burman design, path of steepest ascent determinations and Box-Behnken design. The final optimized fermentation conditions (g/L) included: glucose, 38.877; CaCl2•2H2O, 0.161; temperature, 29.97°C; and inoculation amount, 8.93%. This optimization resulted in an antibacterial activity of 56.13% against FOC4, which was 12.33% higher than that before optimization (43.80%). The results obtained using response surface methodology to optimize the fermentation medium had a significant effect on the production of bioactive metabolites by Streptomyces sp. 1-14. Moreover, during fermentation and storage, pH, light, storage temperature, etc., must be closely monitored to reduce the formation of fermentation products with reduced antibacterial activity. This method is useful for further investigations of the production of anti-FOC4 substances, and could be used to develop bio-control agents to suppress or control banana fusarium wilt.

The Goldilocks Approach: A Review of Employing Design of Experiments in Prokaryotic Recombinant Protein Production

The production of high yields of soluble recombinant protein is one of the main objectives of protein biotechnology. Several factors, such as expression system, vector, host, media composition and induction conditions can influence recombinant protein yield. Identifying the most important factors for optimum protein expression may involve significant investment of time and considerable cost. To address this problem, statistical models such as Design of Experiments (DoE) have been used to optimise recombinant protein production. This review examines the application of DoE in the production of recombinant proteins in prokaryotic expression systems with specific emphasis on media composition and culture conditions. The review examines the most commonly used DoE screening and optimisation designs. It provides examples of DoE applied to optimisation of media and culture conditions.

Optimization of process parameters for ethanol production from sugar cane molasses by Zymomonas mobilis using response surface methodology and genetic algorithm

Ethanol is a potential energy source and its production from renewable biomass has gained lot of popularity. There has been worldwide research to produce ethanol from regional inexpensive substrates. The present study deals with the optimization of process parameters (viz. temperature, pH, initial total reducing sugar (TRS) concentration in sugar cane molasses and fermentation time) for ethanol production from sugar cane molasses by Zymomonas mobilis using Box-Behnken experimental design and genetic algorithm (GA). An empirical model was developed through response surface methodology to analyze the effects of the process parameters on ethanol production. The data obtained after performing the experiments based on statistical design was utilized for regression analysis and analysis of variance studies. The regression equation obtained after regression analysis was used as a fitness function for the genetic algorithm. The GA optimization technique predicted a maximum ethanol yield of 59.59 g/L at temperature 31 °C, pH 5.13, initial TRS concentration 216 g/L and fermentation time 44 h. The maximum experimental ethanol yield obtained after applying GA was 58.4 g/L, which was in close agreement with the predicted value.

Response surface optimization of medium components for naringinase production from Staphylococcus xylosus MAK2

Response surface methodology was used to optimize the fermentation medium for enhancing naringinase production by Staphylococcus xylosus. The first step of this process involved the individual adjustment and optimization of various medium components at shake flask level. Sources of carbon (sucrose) and nitrogen (sodium nitrate), as well as an inducer (naringin) and pH levels were all found to be the important factors significantly affecting naringinase production. In the second step, a 22 full factorial central composite design was applied to determine the optimal levels of each of the significant variables. A second-order polynomial was derived by multiple regression analysis on the experimental data. Using this methodology, the optimum values for the critical components were obtained as follows: sucrose, 10.0%; sodium nitrate, 10.0%; pH 5.6; biomass concentration, 1.58%; and naringin, 0.50% (w/v), respectively. Under optimal conditions, the experimental naringinase production was 8.45 U/mL. The determination coefficients (R(2)) were 0.9908 and 0.9950 for naringinase activity and biomass production, respectively, indicating an adequate degree of reliability in the model.