Combination of uniform design with artificial neural network coupling genetic algorithm: an effective way to obtain high yield of biomass and algicidal compound of a novel HABs control actinomycete

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• 作者：Guanjing Cai (1) (2)
  Wei Zheng (1)
  Xujun Yang (1)
  Bangzhou Zhang (1)
  Tianling Zheng (1) (2)
  
  1. State Key Laboratory of Marine Environmental Science and Key Laboratory of MOE for Coast and Wetland Ecosystems ; School of Life Sciences ; Xiamen University ; No. 422 ; Siming Nan Road ; Xiamen ; 361005 ; China
  2. ShenZhen Research Institute of Xiamen University ; ShenZhen ; 518057 ; China
  
• 关键词：Novel algicidal actinomycete ; Uniform design ; Artificial neural network coupling genetic algorithm ; High yield of biomass and algicidal compound ; HABs control
• 刊名：Microbial Cell Factories
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：13
• 期：1
• 全文大小：434 KB
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• 刊物类别：Chemistry and Materials Science
• 刊物主题：Biotechnology
  Applied Microbiology
  Environmental Engineering/Biotechnology
  
• 出版者：BioMed Central
• ISSN：1475-2859

文摘

Controlling harmful algae blooms (HABs) using microbial algicides is cheap, efficient and environmental-friendly. However, obtaining high yield of algicidal microbes to meet the need of field test is still a big challenge since qualitative and quantitative analysis of algicidal compounds is difficult. In this study, we developed a protocol to increase the yield of both biomass and algicidal compound present in a novel algicidal actinomycete Streptomyces alboflavus RPS, which kills Phaeocystis globosa. To overcome the problem in algicidal compound quantification, we chose algicidal ratio as the index and used artificial neural network to fit the data, which was appropriate for this nonlinear situation. In this protocol, we firstly determined five main influencing factors through single factor experiments and generated the multifactorial experimental groups with a U15(155) uniform-design-table. Then, we used the traditional quadratic polynomial stepwise regression model and an accurate, fully optimized BP-neural network to simulate the fermentation. Optimized with genetic algorithm and verified using experiments, we successfully increased the algicidal ratio of the fermentation broth by 16.90% and the dry mycelial weight by 69.27%. These results suggested that this newly developed approach is a viable and easy way to optimize the fermentation conditions for algicidal microorganisms.