Input-output equations and identifiability of linear ODE models

TL;DR

This paper establishes the theoretical foundations for using input-output equations to verify the structural identifiability of certain linear ODE models, ensuring the method's validity and applicability in practical modeling scenarios.

Contribution

It proves the identifiability of input-output equation coefficients for common linear models, confirming the legitimacy of using these coefficients to analyze model identifiability.

Findings

Coefficients of input-output equations are identifiable in linear models with one output.

The field of identifiable functions is generated by input-output coefficients in these models.

Transfer function coefficients also generate all identifiable functions in strongly connected linear compartment models.

Abstract

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 method of input-output equations is one method for verifying structural identifiability. This method stands out in its importance because the additional insights it provides can be used to analyze and improve models. However, its complete theoretical grounds and applicability are still to be established. A subtlety and key for this method to work correctly is knowing whether the coefficients of these equations are identifiable. In this paper, to address this, we prove identifiability of the coefficients of input-output equations for types of differential models that often appear in practice, such as linear models with one output and linear compartment models in which, from each compartment, one can reach either a leak or an input. This shows that checking identifiability via input-output equations for these models is legitimate and, as we prove, that the field of identifiable functions is generated by the coefficients of the input-output equations. Finally, we exploit a connection between input-output equations and the transfer function matrix to show that, for a linear compartment model with an input and strongly connected graph, the field of all identifiable functions is generated by the coefficients of the transfer function matrix even if the initial conditions are generic.