Elimination of unknowns in a system of differential equations is often required when
analyzing (possibly nonlinear) dynamical systems models, where only a subset of variables …

Computational and mathematical models rely heavily on estimated parameter values for
model development. Identifiability analysis determines how well the parameters of a model …

Parameter identifiability is a structural property of an ODE model for recovering the values of
parameters from the data (ie, from the input and output variables). This property is a …

Structural identifiability is a property of a differential model with parameters that allows for
the parameters to be determined from the model equations in the absence of noise. The …

Structural global parameter identifiability indicates whether one can determine a parameter's
value in an ODE model from given inputs and outputs. If a given model has parameters for …

Structural identifiability is a property of an ODE model with parameters that allows for the
parameters to be determined from continuous noise-free data. This is a natural prerequisite …

Differential equation models are crucial to scientific processes. The values of model
parameters are important for analyzing the behaviour of solutions. A parameter is called …

Structural identifiability concerns the question of which unknown parameters of a model can
be recovered from (perfect) inputoutput data. If all of the parameters of a model can be …

The MEK/ERK signalling pathway is involved in cell division, cell specialisation, survival and
cell death (Shaul and Seger in Biochim Biophys Acta (BBA)-Mol Cell Res 1773 (8): 1213 …

This paper addresses the problem of identifiability of nonlinear polynomial state-space
systems. Such systems have already been studied via the input-output equations, a …