解淀粉芽孢杆菌Q-426培养基优化及抑菌活性的预测

中国生物工程杂志

2013, Vol. 33

Issue (11): 21-26

研究报告

解淀粉芽孢杆菌Q-426培养基优化及抑菌活性的预测

周广麒¹, 马蓬勃¹, 刘俏², 权春善², 范圣第²

1 大连工业大学生物工程学院大连 116034;
2 大连民族学院国家民委-教育部生物化工重点实验室大连 116034

Optimization of Culture Medium and Prediction of Antibacterial Activity by Bacillus Amyloliquefaciens Q-426 Fermentation

ZHOU Guang-qi¹, MA Peng-bo¹, LIU Qiao², QUAN Chun-shan², FAN Sheng-di²

1 School of Biological & Food Engineering, Dalian Polytechnic Univesity, Dalian 116034, China;
2. Key Lab of Bioengineering, the State Ethnic Affairs Commition-Ministry of Education, Dalian 116600, China

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摘要： 为了提高解淀粉芽孢杆菌Q-426发酵生产的抑菌活性，运用响应面法优化解淀粉芽孢杆菌Q-426发酵培养基组分，并利用BP神经网络对抑菌活性进行了预测。优化后培养基配方（g/L）为：葡萄糖3.92、氯化铵0.19、氯化镁3.83、牛肉膏5.00，并以此作为输入数据，将抑菌活性即抑菌圈直径作为输出数据，建立了BP神经网络预测模型。结果表明：优化后抑菌圈直径由24 mm提高到29mm。训练BP神经网络的抑菌圈直径的拟合值与实际值之间的相对误差为-2.962 9%~2.857 1%，相对误差的绝对值的平均值为1.197 9%；测试BP神经网络的抑菌圈直径的预测值与实际值的相对误差为-1.111 1%~1.153 8%，相对误差的绝对值的平均值为0.993 1%，说明建立的基于4个培养基成份的抑菌圈直径BP神经网络预测模型是可行的。

关键词： 解淀粉芽孢杆菌Q-426; 培养基优化; BP神经网络; 抑菌活性预测

Abstract: Improve the antibacterial activity produced by Bacillus amyloliquefaciens Q-426 fermentation. To this end, response surface method is employed to optimize culture medium. The optimized medium is (g/L): glucose 3.92, ammonium chloride 0.19, magnesium chloride 3.83, beef extract 5.00, and the optimization raised the diameter of inhibition zone (DIZ) from 24 mm to 29mm.In addition, we present a model based on BP neural network to predict the DIZ according to the medium components. The BP neural network prediction model is trained using the culture medium components as inputs and the DIZ as output. The fitting error of our prediction model is -2.9629%~2.8571% (absolute mean is 1.1979%); the predicting error is -1.1111%~1.1538% (absolute mean is 0.9931%). Therefore, our study shows the feasibility of the prediction model for DIZ using BP neural network.

Key words: Bacillus amyloliquefaciens Q-426 Culture medium optimization BP neural network Antibacterial activity prediction

收稿日期: 2013-08-09 出版日期: 2013-11-25

ZTFLH:

Q815

通讯作者: 刘俏 E-mail: liuqiao@dlnu.edu.cn.

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引用本文:

周广麒, 马蓬勃, 刘俏, 权春善, 范圣第. 解淀粉芽孢杆菌Q-426培养基优化及抑菌活性的预测[J]. 中国生物工程杂志, 2013, 33(11): 21-26.

ZHOU Guang-qi, MA Peng-bo, LIU Qiao, QUAN Chun-shan, FAN Sheng-di. Optimization of Culture Medium and Prediction of Antibacterial Activity by Bacillus Amyloliquefaciens Q-426 Fermentation. China Biotechnology, 2013, 33(11): 21-26.

链接本文:

https://manu60.magtech.com.cn/biotech/CN/ 或 https://manu60.magtech.com.cn/biotech/CN/Y2013/V33/I11/21

[1] Baileya K L, Boyetchkoa S M. Lngleb T. Social and economic drivers shaping the future of biological control: A Canadian perspective on the factors affecting the development and use of microbial biopesticides.Biological Control, 2010, 52(3):221-229.
[2] Zhao P C, Quan C S, Jin L M, et al. Effects of critical medium components on the production of antifungal lipopeptides from Bacillus amyloliquefaciens Q-426 exhibiting excellent biosurfactant properties.World J Microbiol Biotechnol, 2013, 29:401-409.
[3] 赵鹏超, 权春善, 金黎明, 等.氮源和碳源对解淀粉Q-426抗菌脂肽合成的影响.中国生物工程杂志, 2012, 32(10):50-56. Zhao P C, Quan C S, Jin L M, et al. Nitrogen source and carbon source on the Bacillus amylolique faciens Q-426 effects of antibacterial lipopeptide synthesis. China Biotechnology, 2012, 32(10):50-56.
[4] Ma Y W, Huang M Z, Wan J Q, et al. Prediction model of DnBP degradation based on BP neural network in AAO system.Bioresource Technology, 2011, 102(6):4410-4415.
[5] Reza Pendashteh A, Fakhru'l-Razi A, Chaibakhsh N, et al. Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network.Journal of Hazardous Materials, 2011, 192(2):568-575.
[6] 柳元, 胡山鹰, 陈定江, 等. 人工神经网络用于乙醇发酵生产过程的建模探讨. 计算机与应用化学, 2009, 6(6):998-1002. Liu Y, Hu S Y, Chen D J, et al. Discussion on modeling ethanol fermentation process for artificial neural network.Computers and Applied Chemistry, 2009, 26(6): 998-1002.
[7] 刘淑惢, 薛兴阳, 周苏娟, 等.基于BP神经网络对木瓜蛋白酶法提取鱼腥草多糖工艺的优化.广东药学院学报, 2013, 29(2): 2-5. Liu S R, Xue X Y, Zhou SJ, et al. Optimization of BP neural network on papain extraction process of polysaccharide of Houttuynia cordata Thunb based on. Journal of Guangdong Pharmaceutical University, 2013, 29(2): 2-5.
[8] Yin X L, You Q H, Jiang J H.Optimization of enzyme assisted extraction of polysaccharides from Tricholoma matsutake by response surface methodology.Carbohydrate Polymers, 2011, 86(3):1358-1364.
[9] Qiu J Z, Song F F, Qiu Y F, et al. Optimization of the medium composition of a biphasic production system for mycelial growth and spore production of Aschersonia placenta using response surface methodology.Journal of Invertebrate Pathology, 2013, 112(2):108-115.
[10] 曾勇峰, 权春善, 刘俏, 等.洋葱伯克霍尔菌(Burkholderia cepacia)CF-66发酵生产新型抗菌物质CF66I培养基的优化.中国生物工程杂志, 2006, 26(9):56-60. Zeng Y F, Quan C S, Liu Q, et al. Burke Holzer optimization onion strain CF-66 producing new antibacterial substances in CF66I medium.China Biotechnology, 2006, 26(9):56-60.
[11] 吴有炜.试验设计与数据处理.苏州:苏州大学出版社, 2002.183-186. Wu Y W. Experimental Design and Data Processing. Suzhou: Soochow University Press, 2002.183-186.
[12] 郭洁, 洪子雯, 方晓玲.Box-Behnken实验设计法优化表阿霉素脂质体的处方工艺.复旦学报(医学版).2007, (6):22-23. Guo J, Hong Z W, Fang X L. Preparation of epirubicin liposome Box-Behnken optimization design method. Journal of Fudan University Journal of Medical Sciences 2007, (6):22-23.
[13] 赵亮, 刘俏, 宋莉.用BP神经网络与遗传算法优化γ—氨基丁酸的发酵培养基.计算机与应用学, 2008, 25(10):1273-1276. Zhao L, Liu Q, Song L. The fermentation medium with BP neural network and genetic algorithm optimization of GABA.Computers and Applied Chemistry, 2008, 25 (10):1273-1276.
[14] 邵拥军, 贺辉, 张贻舟, 等. 基于BP 神经网络的湘西金矿成矿预测. 中南大学学报: 自然科学版, 2007, 38(6):1192-1198. Shao Y J, He H, Zhang Y Z, et al. BP neural network based on the Xiangxi gold metallogenic prognosis. Journal of Central South University(Science and Technology), 2007, 38(6):1192-1198.
[15] Sharon Mano Pappu J, Karthik Vijayakumar G, Ramamurthy V.Artificial neural network model for predicting production of Spirulina platensis in outdoor culture.Bioresource Technology, 2013, 130:224-230.
[16] http://www.mathworks.nl/videos/getting-started-with-neural-network-toolbox-68794.html.

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