Repairing dynamic models: a method to obtain identifiable and observable reparameterizations with mechanistic insights

TL;DR

This paper introduces AutoRepar, a method that automatically reparameterizes mechanistic dynamic models to ensure they are structurally identifiable and observable, thereby enhancing their predictive power and mechanistic interpretability.

Contribution

AutoRepar is a novel automated approach that repairs structural deficiencies in dynamic models, maintaining their mechanistic meaning and input-output behavior.

Findings

AutoRepar successfully repairs SIO deficiencies in several literature models.

The reparameterized models retain original dynamics and input-output mappings.

AutoRepar extends STRIKE-GOLDD for automated model correction.

Abstract

Mechanistic dynamic models allow for a quantitative and systematic interpretation of data and the generation of testable hypotheses. However, these models are often over-parameterized, leading to non-identifiability and non-observability, i.e. the impossibility of inferring their parameters and state variables. The lack of structural identifiability and observability (SIO) compromises a model's ability to make predictions and provide insight. Here we present a methodology, AutoRepar, that corrects SIO deficiencies automatically, yielding reparameterized models that are structurally identifiable and observable. The reparameterization preserves the mechanistic meaning of selected variables, and has the exact same dynamics and input-output mapping as the original model. We implement AutoRepar as an extension of the STRIKE-GOLDD software toolbox for SIO analysis, applying it to several models from the literature to demonstrate its ability to repair their structural deficiencies. AutoRepar increases the applicability of mechanistic models, enabling them to provide reliable information about their parameters and dynamics.