Improved Parameter Estimation and Accuracy Using the Bootstrap Method
TL;DRAbstract
In the past, new measurement technologies were developed to measure intracellular metabolites, mRNA and proteins - often referred to as ``omics'' technologies. Parallel to this progression, the development of detailed mathematical models describing metabolic and signal transduction processes becomes very popular. However, knowledge on the dynamical behavior of cellular systems require the estimation of uncertain or even unknown kinetic parameters. Moreover, after estimation of the parameters, a statement on the precision of the parameters is required. A method frequently used in this field is the calculation of the Fisher-Information-Matrix (FIM) to describe the confidence region of the parameters. From a theoretical point of view, the application of the FIM is limited: (i) The FIM gives only lower bounds for the variance of a parameter, since the underlying model equations have nonlinear solutions. (ii) The resulting confidence region is symmetric with respect to the estimated paramet
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In the past, new measurement technologies were developed to measure intracellular metabolites, mRNA and proteins - often referred to as ``omics'' technologies. Parallel to this progression, the development of detailed mathematical models describing metabolic and signal transduction processes becomes very popular. However, knowledge on the dynamical behavior of cellular systems require the estimation of uncertain or even unknown kinetic parameters. Moreover, after estimation of the parameters, a statement on the precision of the parameters is required. A method frequently used in this field is the calculation of the Fisher-Information-Matrix (FIM) to describe the confidence region of the parameters. From a theoretical point of view, the application of the FIM is limited: (i) The FIM gives only lower bounds for the variance of a parameter, since the underlying model equations have nonlinear solutions. (ii) The resulting confidence region is symmetric with respect to the estimated paramet
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