Skip to main content
SpringerLink
Log in

Optimization of media by evolutionary algorithms for production of polyols

  • Published:
Applied Biochemistry and Biotechnology Aims and scope Submit manuscript

Abstract

Biotransformation of sucrose-based medium to polyols has been reported for the first time using osmophilic yeast, Hansenula anomala. A new, real coded evolutionary algorithm was developed for optimization of fermentation medium in parallel shake-flask experiments. By iteratively employing the nature-inspired techniques of selection, crossover, and mutation for a fixed number of generations, the algorithm obtains the optimal values of important process variables, namely, inoculum size and sugar, yeast extract, urea, and MgSO4 concentrations. Maximum polyols yield of 76.43% has been achieved. The method is useful for reducing the overall development time to obtain an efficient fermentation process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Spencer, J. F. T. and Sallans, H. R. (1956), Can. J. Microbiol. 2, 72–79.

    PubMed  CAS  Google Scholar 

  2. Onishi, H. (1960), Bull. Agric. Chem. Soc. Japan 24, 131–140.

    CAS  Google Scholar 

  3. Spencer, J. F. T., Roxburgh, J. M., and Sallans, H. R. (1957), J. Agric. Food. Chem. 5, 64–67.

    Article  CAS  Google Scholar 

  4. Groleau, D. (1995), Biotech. Lett. 17(3), 315–320.

    Article  CAS  Google Scholar 

  5. Button, D. K., Garver, J. C., and Hanjay, G. J. (1966), Appl. Microbiol. 14, 292–294.

    PubMed  CAS  Google Scholar 

  6. Hanjay, G. J., Hendershot, W. F., and Peterson, W. H. (1960), Appl. Microbiol. 8, 5–11.

    Google Scholar 

  7. Onishi, H., Saito, N., and Koshiyama, I. (1961), Agri. Biol. Chem. (Toyko) 25, 124–130.

    CAS  Google Scholar 

  8. Spencer, J. F. T. and Shu, P. (1957), Can. J. Microbiol. 3, 559–567.

    Article  PubMed  CAS  Google Scholar 

  9. Ramachandran, N. and Sulebele, G. (1979), Ind. J. Microbiol. 9, 136–141.

    Google Scholar 

  10. Patil, S. V. and Borawake, S. D. (1989), in Proceedings of Thirty-Ninth Annual Convention of DSTA, Ravetkar, D. M., ed., The Deccan Sugar Technologists’ Association, Pune, India, pp. B-1–B-6.

    Google Scholar 

  11. Somogyi, M. (1944), J. Biol. Chem. 160, 61–66.

    Google Scholar 

  12. Nelson, N. (1945), J. Biol. Chem. 163, 375–380.

    Google Scholar 

  13. Lambert, N. and Neish, A. C. (1950), Can. J. Res. 28(sect. B) 83–90.

    Google Scholar 

  14. Achary, A., Hariharan, K. A., Bandhyopadhyaya, S., et al. (1997), Biotechnol. Bioeng. 55, 148–154.

    Article  CAS  Google Scholar 

  15. Hounsa, C. G. (1996), Appl. Microbiol. Biotechnol. 45, 764–770.

    Article  PubMed  CAS  Google Scholar 

  16. Lee, S. L. and Chen, W. C. (1997), Enzyme Microb. Technol. 21, 436–440.

    Article  CAS  Google Scholar 

  17. Zhu, Y., Knol, W., Smits, J. P., et al. (1996), Enzyme Microb. Technol. 18, 108–112.

    Article  CAS  Google Scholar 

  18. Glassey, J., Ignova, M., Ward, A. C., et al. (1997), J. Biotech. 52, 201–205.

    Article  CAS  Google Scholar 

  19. Montague, G. and Morris, A. (1994), TIBTECH 12, 312–323.

    CAS  Google Scholar 

  20. Glassey, J., Montague, G. A., Ward, A. C., et al. (1994), Biotechnol. Bioeng. 44, 397–405.

    Article  CAS  Google Scholar 

  21. Holland, J. H. (1986), in Induction: Process of Inference, Learning and Discovery, MIT Press.

  22. Goldberg, D. E. (1989), in Genetic Algorithm in Search, Optimization and Machine Learning, Addison-Wesley.

  23. Freyer, S., Weuster-Botz, D., and Wandrey, C. (1992), Bioengineering (Graefelfing, Fed. Repub. Ger.) 8(5+6), 16–25.

    CAS  Google Scholar 

  24. Matsuura, K., Shiba, H., Nunokawa, Y., et al. (1993), Seibutsu Kogaku Kaishi. 71(3), 171–178.

    CAS  Google Scholar 

  25. Weuster-Botz, D., Kelle, R., Frantzen, M., et al. (1997), Biotechnol. Prog. 13(4), 387–393.

    Article  CAS  Google Scholar 

  26. Bohling, H. and Voss, H. (1997), Bioforum Int. 1(2), 86, 88–90.

    CAS  Google Scholar 

  27. Beste, H., Fackeldey, M., Willems, M., et al. (1997), Chem. Eng. Technol. 20(6), 403–413.

    Article  CAS  Google Scholar 

  28. Zuzek, M., Friedrich, J., Cestnik, B., et al. (1996), Biotechnol. Tech. 10(12), 991–996.

    CAS  Google Scholar 

  29. Weuster-Botz, D. and Wandrey, C. (1995), Process Biochem. 30(6), 563–571.

    Article  CAS  Google Scholar 

  30. Deb, K. and Agrawal, R. B. (1955), Complex Systems 9, 115–148.

    MathSciNet  Google Scholar 

  31. Michalewicz, Z. (1994), in Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, New York, Chapter 5, p. 95.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Alcohol Technology, Vasantdada Sugar Institute, 412 307, Pune, India

    S. V. Patil

  2. National Chemical Laboratory, Chemical Engineering Division, 411 008, Pune, India

    V. K. Jayaraman & B. D. Kulkarni

Authors
  1. S. V. Patil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. V. K. Jayaraman
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. D. Kulkarni
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. D. Kulkarni.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Patil, S.V., Jayaraman, V.K. & Kulkarni, B.D. Optimization of media by evolutionary algorithms for production of polyols. Appl Biochem Biotechnol 102, 119–128 (2002). https://doi.org/10.1385/ABAB:102-103:1-6:119

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1385/ABAB:102-103:1-6:119

Index Entries

Search

Navigation