Mnif Ines, Chaabouni-Ellouze Semia, Ghribi Dhouha
Unité Enzymes et Bioconversion, Ecole Nationale d'Ingénieurs de Sfax, BP W 3038, Sfax, Tunisia.
Biotechnol Res Int. 2012;2012:795430. doi: 10.1155/2012/795430. Epub 2012 May 7.
Nutritional requirements can contribute considerably to the production cost and the bioprocess economics. Media optimisation using response surface methodology is one of the used methods to ameliorate the bioprocess economics. In the present study, biosurfactant production by Bacillus subtilis SPB1 was effectively enhanced by response surface methodology. A Plackett-Burman-based statistical screening procedure was adopted to determine the most important factor affecting lipopeptide production. Eleven variables are screened and results show that glucose, K(2)HPO(4), and urea concentrations influence the most biosurfactant production. A Central Composite Design was conducted to optimize the three selected factors. Statistical analyses of the data of model fitting were done by using NemrodW. Results show a maximum predicted biosurfactant concentration of 2.93 (±0.32) g/L when using 15 g/L glucose, 6 g/L urea, and 1 g/L K(2)HPO(4). The predicted value is approximately 1.65 much higher than the original production determined by the conventional one-factor-at-a-time optimization method.
营养需求会对生产成本和生物过程经济性产生相当大的影响。使用响应面法进行培养基优化是改善生物过程经济性的常用方法之一。在本研究中,通过响应面法有效地提高了枯草芽孢杆菌SPB1的生物表面活性剂产量。采用基于Plackett-Burman的统计筛选程序来确定影响脂肽生产的最重要因素。筛选了11个变量,结果表明葡萄糖、K₂HPO₄和尿素浓度对生物表面活性剂产量的影响最大。进行了中心复合设计以优化所选的三个因素。使用NemrodW对模型拟合数据进行统计分析。结果表明,当使用15 g/L葡萄糖、6 g/L尿素和1 g/L K₂HPO₄时,预测的生物表面活性剂最大浓度为2.93(±0.32)g/L。该预测值比通过传统的一次单因素优化方法确定的原始产量高出约1.65倍。