Structural identifiability and sensitivity.

Ordinary differential equation models often contain a large number of parameters that must be determined from measurements by estimation procedure. For an estimation to be successful there must be a unique set of parameters that can have produced the measured data. This is not the case if a model is not structurally identifiable with the given set of inputs and outputs. The local identifiability of linear and nonlinear models was investigated by an approach based on the rank of the sensitivity matrix of model output with respect to parameters. Associated with multiple random drawn of parameters used as nominal values, the approach reinforces conclusions regarding the local identifiability of models. The numerical implementation for obtaining the sensitivity matrix without any approximation, the extension of the approach to multi-output context and the detection of unidentifiable parameters were also discussed. Based on elementary examples, we showed that (1°) addition of nonlinear elements switches an unidentifiable model to identifiable; (2°) in the presence of nonlinear elements in the model, structural and parametric identifiability are connected issues; and (3°) addition of outputs or/and new inputs improve identifiability conditions. Since the model is the basic tool to obtain information from a set of measurements, its identifiability must be systematically checked.