Optimization of media constituents through response surface methodology for improved production of alkaline proteases by Serratia rubidaea

Abstract

BACKGROUND: Response surface methodology is used to build a predictive model of the combined effects of independent variables (pH, salt concentration starch and casein). The model was validated in a laboratory-scale bioreactor for extracellular protease production from a newly isolated Serratia rubidaea.

Enhanced fibrinolytic protease production by Serratia marcescens RSPB11 through Plackett-Burman and response surface methodological approaches

“…Figure 2(a) clarifies that serralysin production is dependent on interaction of dextrose and casein and is also evidenced by their p-value of 0.001 (Table 6) which is highly significant. Similar effect between carbon and nitrogen source has been observed in case of protease production by Serratia rubidea [15]. Interaction of casein with other variables like KH 2 PO 4 , CaCl 2 and inoculum can be observed in Figure 2(e, f, g), where > 24,000 U/ml serralysin can be yielded if the casein concentration is around 3% (w/v) in the production media.…”

“…In addition, response surface methodology (RSM) is an efficient strategic experimental tool by which the optimal conditions of a multivariable system may be determined [12]. A combination of Plackett-Burman and RSM was applied successfully for optimizing process parameters for the production of different biomolecules from different microbes like protease, asparginase from Bacillus sp., Serratia rubidea [13][14][15], laccase from Coriolus versicolor [16], fibrinolytic protease from gram negative Bacillus sp., [17], lipase from Rhodotorula sp., MTCC 8737 [18], tannase from Aspergillus niger [19], polysaccharide from Neisseria meningitides [20], epothilone-B from Sorangium cellulosum [21]. A hybrid system of feed-forward neural network (FFNN) and genetic algorithm (GA) was used to optimize the fermentation conditions to enhance the alkaline protease production by Bacillus circulans [22].…”

Removal of 4-chlorophenol from aqueous solution by granular activated carbon/nanoscale zero valent iron based on Response Surface Modeling

“…The complete design consisted of 60 runs and these were performed in duplicate to optimize the levels of selected variables (initial concentration of 4-CP, pH, contact time, adsorbent dose, and type of adsorbent). For statistical calculations, the fi ve independent variables were designated as X 1 , X 2 , X 3 , X 4 and X 5 , respectively, and were coded according to the following equation (Yazdanbakhsh and Hashempour 2015, Doddapaneni et al 2007, Moradi et al 2016:…”

Optimization of Cd2+ removal by the cyanobacterium Synechocystis pevalekii using the response surface methodology

“…where x i is the coded value of an independent variable, X i is the real value of an independent variable, X 0 is the real value of an independent variable at the centre point and DX i is the step change value [20]. The lowest and highest levels of the variables were pH 5 and 7, biomass concentration 0.025 and 0.3 mg protein ml À1 and metal concentration 3 and 7 mg ml À1 .…”