Biotechnology Letters

, Volume 25, Issue 21, pp 1837–1842

Optimization of a fermentation medium using neural networks and genetic algorithms



Artificial neural networks and genetic algorithms are used to model and optimize a fermentation medium for the production of the enzyme hydantoinase by Agrobacterium radiobacter. Experimental data reported in the literature were used to build two neural network models. The concentrations of four medium components served as inputs to the neural network models, and hydantoinase or cell concentration served as a single output of each model. Genetic algorithms were used to optimize the input space of the neural network models to find the optimum settings for maximum enzyme and cell production. Using this procedure, two artificial intelligence techniques have been effectively integrated to create a powerful tool for process modeling and optimization.

artificial neural network genetic algorithm medium optimization response surface methodology 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Achary A, Hariharan KA, Bandhyopadhyaya S, Ramachandran R, Jayaraman K (1997) Application of numerical modeling for the development of optimized complex medium for D-hydantoinase production from Agrobacterium radiobacter NRRL B 11291. Biotechnol. Bioeng. 55: 148–154.CrossRefGoogle Scholar
  2. Baishan F, Hongwen C, Xiaolan X, Ning W, Zongding H (2003) Using genetic algorithms coupling neural networks in a study of xylitol production: medium optimization. Proc. Biochem. 38: 979–985.Google Scholar
  3. Baughman DR, Liu YA (1995) Neural Networks in Bioprocessing and Chemical Engineering. San Diego: Academic Press.Google Scholar
  4. Cheema JJS, Sankpal NV, Tambe SS, Kulkarni BD (2002) Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation. Biotechnol. Prog. 18: 1356–1365.PubMedGoogle Scholar
  5. Goldberg D (1989) Genetic Algorithms in Search, Optimization, and Machine Learning. Reading: Addison-Wesley.Google Scholar
  6. Holland J (1975) Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.Google Scholar
  7. Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2: 359–366.Google Scholar
  8. Houck CR, Joines JA, Kay MG (1995) A Genetic Algorithm for Function Optimization: AMatlab Implementation. Technical Report NCSU-IE TR 95-09. Raleigh, NC: North Carolina State University.Google Scholar
  9. Kennedy M, Krouse D (1999) Strategies for improving fermentation medium performance: a review. J. Ind. Microbiol. Biotechnol. 23: 456–475.Google Scholar
  10. Liu CH, Hwang CF, Liao CC (1999) Medium optimization for glutathione production by Saccharomyces cerevisiae. Proc. Biochem. 34: 17–23.Google Scholar
  11. Marteijn RCL, Jurrius O, Dhont J, de Gooijer CD, Tramper J, Martens DE (2003) Optimization of a feed culture medium for fed-batch culture of insect cells using a genetic algorithm. Biotechnol. Bioeng. 81: 269–278.PubMedGoogle Scholar
  12. Patil SV, Jayaraman VK, Kulkarni BD (2002) Optimization of media by evolutionary algorithms for production of polyols. Appl. Biochem. Biotechnol. 102: 119–128.PubMedGoogle Scholar
  13. Weuster-Botz D (2000) Experimental design for fermentation media development: statistical design or global random search? J. Biosci. Bioeng. 90: 473–483.Google Scholar

Copyright information

© Kluwer Academic Publishers 2003

Authors and Affiliations

  1. 1.Department of Chemical and Process EngineeringUniversity of CanterburyChristchurchNew Zealand

Personalised recommendations