Research Article

Enrichment of glutaminase production by Bacillus subtilis RSP-GLU in submerged cultivation based on neural network—genetic algorithm approach

  1. Thadikamala Sathish,
  2. Reddy Shetty Prakasham*

Article first published online: 5 OCT 2009

DOI: 10.1002/jctb.2267

Issue

Journal of Chemical Technology and Biotechnology

Journal of Chemical Technology and Biotechnology

Volume 85, Issue 1, pages 50–58, January 2010

Additional Information

How to Cite

Sathish, T. and Prakasham, R. S. (2010), Enrichment of glutaminase production by Bacillus subtilis RSP-GLU in submerged cultivation based on neural network—genetic algorithm approach. J. Chem. Technol. Biotechnol., 85: 50–58. doi: 10.1002/jctb.2267

Author Information

  1. Bioengineering and Environmental Centre, Indian Institute of Chemical Technology, Hyderabad—500 607, India

*Bioengineering and Environmental Centre, Indian Institute of Chemical Technology, Hyderabad—500 607, India.

Publication History

  1. Issue published online: 1 DEC 2009
  2. Article first published online: 5 OCT 2009
  3. Manuscript Accepted: 19 AUG 2009
  4. Manuscript Revised: 24 JUN 2009
  5. Manuscript Received: 9 APR 2009

Funded by

  • Council of Scientific and Industrial Research (CSIR), New Delhi

SEARCH

SEARCH BY CITATION

ARTICLE TOOLS

View Full Article (HTML) Get PDF (350K)

Keywords:

  • Bacillus subtilis RSP-GLU;
  • glutaminase;
  • genetic algorithm;
  • neural networks;
  • optimization

Abstract

BACKGROUND: Owing to the importance of glutaminase in biotech product production, its production with isolated Bacillus subtilis RSP-GLU (MTCC 9727) was investigated. Fermentation factors play an important role in product enhancement. Hence, glutaminase production was optimized using an artificial neural network (ANN) coupled genetic algorithm (GA).

RESULTS: A ‘6–12–1’ topology ANN was constructed to identify the nonlinear relationship between fermentation factors and enzyme yield. ANN-predicted values were optimized for glutaminase production using a GA. The overall mean absolute predictive error (MAPE) and the mean square errors (MSE) were observed to be 0.00125 and 1.77 and 0.002 and 3.06 for training and testing, respectively. The goodness of neural network prediction (coefficient of R2) was found to be 0.996. The maximum interactive impact on glutaminase production was noted with rpm versus medium volume. The use of ANN–GA hybrid methodology resulted in a significant improvement (47%) in glutaminase yield.

CONCLUSION: Five different optimum fermentation conditions out of 500 revealed maximum enzyme production. Glutaminase enzyme production in this Bacillus subtilis RSP-GLU is strongly influenced by aeration of the fermentation. A hybrid ANN-GA effectively identifies the different fermentation conditions for optimum production of enzyme in a given large set of conditions. Copyright © 2009 Society of Chemical Industry

View Full Article (HTML) Get PDF (350K)