  Suppose we wish to predict the behaviour of a physical system. We may choose
to represent the system by model structure $S$ (a set of related mathematical
models defined by parametric relationships between system variables), and a
parameter set $\Theta$. Each parameter vector in $\Theta$ is associated with a
completely specified model in $S$. We use $S$ with system observations in
estimating the "true" (unknown) parameter vector. Inconveniently, multiple
parameter vectors may cause $S$ to approximate the data equally well. If we
cannot distinguish between such alternatives, and these lead to dissimilar
predictions, we cannot confidently use $S$ in decision making. This result may
render efforts in data collection and modelling fruitless. This outcome occurs
when $S$ lacks the property of structural global identifiability (SGI).
Fortunately, we can test various classes of structures for SGI prior to data
collection. A non-SGI result may guide changes to our structure or experimental
design towards obtaining a better outcome. We aim to assist the testing of
structures for SGI through bespoke Maple 2020 procedures. We consider
continuous-time, uncontrolled, linear time-invariant state-space structures.
Here, the time evolution of the state-variable vector ${\bf x}$ is modelled by
a system of constant-coefficient, ordinary differential equations. We utilise
the "transfer function" approach, which is also applicable to the
"compartmental" subclass (mass is conserved). Our use of Maple's "Explore"
enables an interactive consideration of a parent structure and its variants,
obtained as the user changes which components of ${\bf x}$ are observed, or
have non-zero initial conditions. Such changes may influence the information
content of the idealised output available for the SGI test, and hence, its
result. Our approach may inform the interactive analysis of structures from
other classes.
