Exploiting the bootstrap method for quantifying parameter confidence intervals in dynamical systems.
A quantitative description of dynamical systems requires the estimation of uncertain kinetic parameters and an analysis of their precision. A method frequently used to describe the confidence intervals of estimated parameters is based on the Fisher-Information-Matrix. The application of this traditional method has two important shortcomings: (i) it gives only lower bounds for the variance of a parameter if the solution of the underlying model equations is non-linear in parameters. (ii) The resulting confidence interval is symmetric with respect to the estimated parameter. Here, we show that by applying the bootstrap method a better approximation of (possibly) asymmetric confidence intervals for parameters could be obtained. In contrast to previous applications devoted to non-parametric problems, a dynamical model describing a bio-chemical network is used to evaluate the method.
Joshi
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Institut für Verfahrenstechnik, Otto-von-Guericke UniversitätMagdeburg, Universitätsplatz 2, Magdeburg, Germany.
Seidel-Morgenstern
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Kremling
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D000465:Algorithms; D002468:Cell Physiological Phenomena; D003198:Computer Simulation; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000502:physiology*
D020543:Proteome