  Structural identifiability is an important property of parametric ODE models.
When conducting an experiment and inferring the parameter value from the
time-series data, we want to know if the value is globally, locally, or
non-identifiable. Global identifiability of the parameter indicates that there
exists only one possible solution to the inference problem, local
identifiability suggests that there could be several (but finitely many)
possibilities, while non-identifiability implies that there are infinitely many
possibilities for the value. Having this information is useful since, one
would, for example, only perform inferences for the parameters which are
identifiable. Given the current significance and widespread research conducted
in this area, we decided to create a database of linear compartment models and
their identifiability results. This facilitates the process of checking
theorems and conjectures and drawing conclusions on identifiability. By only
storing models up to symmetries and isomorphisms, we optimize memory efficiency
and reduce query time. We conclude by applying our database to real problems.
We tested a conjecture about deleting one leak of the model states in the paper
'Linear compartmental models: Input-output equations and operations that
preserve identifiability' by E. Gross et al., and managed to produce a
counterexample. We also compute some interesting statistics related to the
identifiability of linear compartment model parameters.
