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Folia Microbiologica

, Volume 58, Issue 5, pp 393–401 | Cite as

Real encoded genetic algorithm and response surface methodology to optimize production of an indolizidine alkaloid, swainsonine, from Metarhizium anisopliae

  • Digar Singh
  • Gurvinder KaurEmail author
Article

Abstract

Response surface methodology (RSM) and artificial neural network-real encoded genetic algorithm (ANN-REGA) were employed to develop a process for fermentative swainsonine production from Metarhizium anisopliae (ARSEF 1724). The effect of finally screened process variables viz. inoculum size, oatmeal extract, glucose, and CaCl2 were investigated through central composite design and were further utilized for training sets in ANN with training and test R values of 0.99 and 0.94, respectively. ANN-REGA was finally employed to simulate the predictive swainsonine production with best evolved media composition. ANN-REGA predicted a more precise fermentation model with 103 % (shake flask) increase in alkaloid production compared to 75.62 % (shake flask) obtained with RSM model upon validation.

Keywords

Response Surface Methodology Shake Flask Inoculum Size Alkaloid Production Metarhizium Anisopliae 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abbreviations

CDW

Cell dry weight

RSM

Response surface methodology

CCD

Central composite design

PB

Plackett–Burman

OFAT

One factor at a time

GA

Genetic algorithm

ANN

Artificial neural network

ANN-REGA

Artificial neural network-real encoded genetic algorithm

Notes

Acknowledgments

We thank Indian Institute of Technology Guwahati, for providing the experimental facilities, and the Council of Scientific and Industrial research, PUSA, New Delhi, Government of India, for providing the research fellowship.

Conflict of interest

None

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Copyright information

© Institute of Microbiology, Academy of Sciences of the Czech Republic, v.v.i. 2013

Authors and Affiliations

  1. 1.Department of BiotechnologyIndian Institute of Technology GuwahatiGuwahatiIndia

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