Speaker
Description
Structural identifiability answers a theoretical question about which parameter combinations of a mathematical model are uniquely recovered from ideal, noise-free input–output data. Identifiability has been long studied on linear compartment models, and is closely related to the algebraic structure of the input-output equation obtained from the model. In this work, we investigate how structural identifiability is affected under singular limits in which a single parameter of a model is sent to infinity. Such limits induce a collapse of the underlying linear compartment graph that resembles an edge contraction and results in a reduced model. Here, we show that the transfer function of the original model converges to the transfer function of the collapsed model. From this result, we can identify which parameter combinations survive the singular limit and characterize the resulting loss of identifiability. We illustrate these results on several families of linear compartment models, including mammillary, catenary, and cyclic networks, and discuss connections to model reduction and the geometry of parameter space under asymptotic limits.
