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.
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References
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)
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)
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)
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)
Shuler, M.L., Kargi, F.: Bioprocess engineering: basic concepts. Prentice-Hall, Englewood Cliffs (2001)
Eilers, P.H.C.: A Perfect Smoother. Analytical Chemistry 75(14), 3631–3636 (2003)
Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. Journal 7, 308–313 (1965)
Nelles, O.: Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer, Germany (2001)
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Montera, L., Horta, A.C.L., Zangirolami, T.C., do Carmo Nicoletti, M., Carmo, T.S., Gonçalves, V.M. (2008). A Heuristic Search for Optimal Parameter Values of Three Biokinetic Growth Models for Describing Batch Cultivations of Streptococcus Pneumoniae in Bioreactors. In: Nguyen, N.T., Borzemski, L., Grzech, A., Ali, M. (eds) New Frontiers in Applied Artificial Intelligence. IEA/AIE 2008. Lecture Notes in Computer Science(), vol 5027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69052-8_38
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DOI: https://doi.org/10.1007/978-3-540-69052-8_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69045-0
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