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A Heuristic Search for Optimal Parameter Values of Three Biokinetic Growth Models for Describing Batch Cultivations of Streptococcus Pneumoniae in Bioreactors

  • Luciana Montera
  • Antonio C. L. Horta
  • Teresa C. Zangirolami
  • Maria do Carmo Nicoletti
  • Talita S. Carmo
  • Viviane M. Gonçalves
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5027)

Abstract

Simulated annealing (SA) is a stochastic search procedure which can lead to a reliable optimization method. This work describes a dynamic mathematical model for Streptococcus pneumoniae batch cultivations containing 8 unknown parameters, which were calibrated by a SA algorithm through the minimization of an evaluation function based on the performance of the model on real experimental data. Three kinetic expressions, the Monod, Moser and Tessier equations, commonly employed to describe microbial growth were tested in the model simulations. SA convergence was achieved after 13810 interactions (about 10 minutes of computing time) and the Tessier equation was identified as the kinetic expression which provided the best fit to the cultivation dataset used for parameter estimation. The model comprising the Tessier equation, estimated parameter values supplied by SA and mass balance equations was further validated by comparing the simulated results to 3 experimental datasets from new cultivations carried out in similar conditions.

Keywords

simulated annealing microbial growth models biokinetic parameters estimation 

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References

  1. 1.
    Moles, C.G., Mendes, P., Banga, J.R.: Parameter estimation in biochemical pathways: a comparison of global optimization methods. Genome Res. 13, 2467–2474 (2003)CrossRefGoogle Scholar
  2. 2.
    Bonilla-Petriciolet, A., Bravo-Snchez, U.I., Castillo-Borja, F., Zapiain-Salinas, J.G., Soto-Bernal, J.J.: The performance of simulated annealing parameter estimation for vapor-liquid equilibrium modeling. Br. J. of Chem. Eng. 24, 151–162 (2007)Google Scholar
  3. 3.
    Rodriguez-Fernandez, M., Mendes, P., Banga, J.R.: A hybrid approach for efficient and robust parameter estimation in biochemical pathways. BioSystems 83, 248–265 (2006)CrossRefGoogle Scholar
  4. 4.
    Gonçalves, V.M., Zangirolami, T.C., Giordano, R.L.C., Raw, I., Tanizaki, M.M., Giordano, R.C.: Optimization of medium and cultivation conditions for capsular polysaccharide production by Streptococcus pneumoniae serotype 23F. Appl. Microbiol. Biotechnol. 59, 713–717 (2002)CrossRefGoogle Scholar
  5. 5.
    Shuler, M.L., Kargi, F.: Bioprocess engineering: basic concepts. Prentice-Hall, Englewood Cliffs (2001)Google Scholar
  6. 6.
    Eilers, P.H.C.: A Perfect Smoother. Analytical Chemistry 75(14), 3631–3636 (2003)CrossRefGoogle Scholar
  7. 7.
    Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. Journal 7, 308–313 (1965)zbMATHGoogle Scholar
  8. 8.
    Nelles, O.: Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Germany (2001)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Luciana Montera
    • 1
  • Antonio C. L. Horta
    • 1
  • Teresa C. Zangirolami
    • 2
  • Maria do Carmo Nicoletti
    • 3
  • Talita S. Carmo
    • 4
  • Viviane M. Gonçalves
    • 4
  1. 1.PPG-BiotechnologyUFSCar, SPBrazil
  2. 2.Dept. of Chemical EngineeringUFSCar, SPBrazil
  3. 3.Dept. of Computer ScienceUFSCar, SPBrazil
  4. 4.Butantan Institute, SPBrazil

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