Structural identifiability of partially-observed stochastic processes: from single-particle trajectories to total particle density data

TL;DR

This paper develops a new methodology to analyze the structural identifiability of stochastic processes, comparing single-particle trajectories and total density data, with applications to biological models.

Contribution

It introduces a novel approach combining differential algebra and Taylor expansion techniques for identifiability analysis of stochastic models.

Findings

Trajectory data allows full parameter identifiability.

Density data only provides local identifiability.

Initial conditions critically influence identifiability conclusions.

Abstract

The increasing availability of experimental data has intensified interest in calibrating stochastic models, raising fundamental questions about parameter identifiability. Structural identifiability determines whether parameters can be uniquely recovered from idealised, noise-free data, a prerequisite to allow for parameter estimation. However, existing methods to assess structural identifiability are not generally applicable to stochastic processes. We develop a methodology to analyse structural identifiability for a class of spatio-temporal stochastic processes. We investigate how identifiability depends on the type of available data, distinguishing between single-particle trajectories and total particle density measurements. For trajectory data, we use the individual-based model description that explicitly represents single-particle dynamics. For population-level data, we derive a partial differential equation model representation, that describes the evolution of total particle density, and apply a differential algebra approach, common to ordinary differential equations analysis. We further introduce a novel method to study the initial condition, based on characteristic equations to construct a Taylor expansion of the density evolution, enabling identification of additional identifiable parameter combinations. We apply our methodology to a model, and show it is identifiable with trajectory data but only locally identifiable with density data, and demonstrate the critical role of initial conditions in the identifiability analysis.