应用遗传算法和神经网络优化多杀菌素发酵培养基  

Optimization of the Spinosad Fermentation Medium by Applying Genetic Algorithm and Neural Network

作　　者：张峰 吴柳娟 李躺 王灿 胡益波 丁学知[1] 夏立秋 

机构地区：[1]湖南师范大学生命科学学院微生物分子生物学湖南省重点实验室省部共建淡水鱼类发育生物学国家重点实验室,中国长沙410081

出　　处：《湖南师范大学自然科学学报》2017年第5期36-43,共8页Journal of Natural Science of Hunan Normal University

基　　金：国家"973"计划资助项目(2011CB111680);国家"863"计划资助项目(NC2010GA0091);国家自然科学资助基金(31070006);湖南省"生物发育工程及新产品研发协同创新中心"资助项目(20134486)

摘　　要：通过优化刺糖多孢菌发酵合成多杀菌素培养基成分,改善培养条件,从而提高多杀菌素产量.在单因素以及Plackett-Burman试验设计的基础上,采用Box-Behnken试验设计方法对发酵培养基组分中的玉米浆、可溶性淀粉、丙酸钠进行研究,运用遗传算法优化的BP神经网络建立多杀菌素产量与培养基组分浓度之间的预测模型,采用循环算法对此模型进行寻优,得到三种组分的最佳配比为:玉米浆7 g/L、可溶性淀粉16 g/L、丙酸钠2 g/L,多杀菌素产量达到(550.22±3.84)mg/L,采用上述方法优化后的培养基使得多杀菌素产量比原始培养基产量(225mg/L)提高145%.本研究结果可为培养基优化提供一种有效的建模方法.In this work, we report an approach to improve spinosad production by optimizing the fermentation medium components. Com steep liquor, soluble starch and sodium propionate in the fermentation medium were in-vestigated by Box-Behnken design （BBD） , which was based on single factor and the Plackett-Burman design. Moreover, a prediction model of the spinosad yield as a function of the medium component concentration has been established by using the artificial neural network （ ANN） optimized by genetic algorithm （ GA） . Using ANN opti-mized by GA as the objective function, we employed the circulatory algorithm to optimize the medium components and the optimal ratio of the three components as follows： with com steep liquor 7 g/ L, soluble starch 16 g/L, and sodium propionate 2 g/L,the final yield of spinosad reached （550. 22 ± 3. 84 ） mg/L, which was 145% higher than the original fermentatiom medium（225 mg/L） obtained when cultured on the optimized medium. Our results from this study can provide an effective modeling method for medium optimization.

关 键 词：刺糖多孢菌 多杀菌素 发酵培养基优化 神经网络 遗传算法 

分 类 号：S482.3[农业科学—农药学] TQ927[农业科学—植物保护]