Li Zhongwei, Sun Beibei, Xin Yuezhen, Wang Xun, Zhu Hu
College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, China.
College of Computer and Communication Engineering, China University of Petroleum, Qingdao, Shandong 266580, China; Center for Bioengineering and Biotechnology, China University of Petroleum, Qingdao, Shandong 266580, China.
Biomed Res Int. 2016;2016:4374603. doi: 10.1155/2016/4374603. Epub 2016 Aug 9.
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer. Because of the great medicinal value, there are large demands on flavones for medical use and research. Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially. The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem. Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL. In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed. Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments. Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.
黄酮是桑黄真菌的次生代谢产物，具有抗氧化和抗癌特性。由于其巨大的药用价值，医用黄酮及相关研究对黄酮有大量需求。从天然桑黄中提取的黄酮无法满足医学和研究需求，因为天然环境中的桑黄非常稀少且难以人工栽培。黄酮的生产主要与桑黄的发酵培养有关，这使得培养条件的优化成为一个重要问题。人们进行了一些研究来优化发酵培养条件，比如采用响应面法，该方法称黄酮的最佳产量为1532.83μg/mL。为了进一步优化黄酮的发酵培养条件，本文提出了一种将遗传算法和BP神经网络相结合的混合智能算法。我们的方法具有智能学习能力，能够克服大规模生物实验的局限性。通过模拟，获得了最佳培养条件，黄酮产量提高到了2200μg/mL。
