A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network

Joint Authors

Li, Zhongwei
Xin, Yuezhen
Wang, Xun
Sun, Beibei
Zhu, Hu

Source

BioMed Research International

Issue

Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-6, 6 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-08-09

Country of Publication

Egypt

No. of Pages

6

Main Subjects

Medicine

Abstract EN

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.

American Psychological Association (APA)

Li, Zhongwei& Sun, Beibei& Xin, Yuezhen& Wang, Xun& Zhu, Hu. 2016. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1097782

Modern Language Association (MLA)

Li, Zhongwei…[et al.]. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International No. 2016 (2016), pp.1-6.
https://search.emarefa.net/detail/BIM-1097782

American Medical Association (AMA)

Li, Zhongwei& Sun, Beibei& Xin, Yuezhen& Wang, Xun& Zhu, Hu. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1097782

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1097782