Generalized sensitivity functions in physiological system identification.

Parameters of physiological models are commonly associated in an input-output experiment with a specific pattern of the system response. This association is often made on an intuitive basis by traditional sensitivity analysis, i.e., by inspecting the variations of model output trajectories with respect to parameter variations. However, this approach provides limited information since, for instance, it ignores correlation among parameters. The aim of this study is to propose a new set of sensitivity functions, called the generalized sensitivity functions (GSF), for the analysis of input-output identification experiments. GSF are based on information theoretical criteria and provide, as compared to traditional sensitivity analysis, a more accurate picture on the information content of measured outputs on individual model parameters at different times. Case studies are presented on an input-output model and on two structural circulatory and respiratory models. GSF allow the definition of relevant time intervals for the identification of specific parameters and improve the understanding of the role played by specific model parameters in describing experimental data.