Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators

Parameter identifiability is often requisite to the effective application of mathematical models in the interpretation of biological data, however theory applicable to the study of partial differential equations remains limited. We present a new approach to structural identifiability analysis of fully observed parabolic equations that are linear in their parameters. Our approach frames identifiability as an existence and uniqueness problem in a closely related elliptic equation and draws, for homogeneous equations, on the well-known Fredholm alternative to establish unconditional identifiability, and cases where specific choices of initial and boundary conditions lead to non-identifiability. While in some sense pathological, we demonstrate that this loss of structural identifiability has ramifications for practical identifiability; important particularly for spatial problems, where the initial condition is often limited by experimental constraints. For cases with nonlinear reaction terms, uniqueness of solutions to the auxiliary elliptic equation corresponds to identifiability, often leading to unconditional global identifiability under mild assumptions. We present analysis for a suite of simple scalar models with various boundary conditions that include linear (exponential) and nonlinear (logistic) source terms, and a special case of a two-species cell motility model. We conclude by discussing how this new perspective enables well-developed analysis tools to advance the developing theory underlying structural identifiability of partial differential equations.

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Publication Details

Journal
Journal of Mathematical Biology
Published
2025-05-29
DOI
https://doi.org/10.1007/s00285-025-02225-w
Citations
4
Primary Topic
Mathematical Biology Tumor Growth
Type
article
Field-Weighted Citation Impact
5.47

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article

Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators

Alexander P. Browning, Yurij Salmaniw
4 citations
Journal of Mathematical Biology
Mathematical Biology Tumor Growth
5.47
article

Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators

Alexander P. Browning, Yurij Salmaniw
article en
4 citations

Abstract

Parameter identifiability is often requisite to the effective application of mathematical models in the interpretation of biological data, however theory applicable to the study of partial differential equations remains limited. We present a new approach to structural identifiability analysis of fully observed parabolic equations that are linear in their parameters. Our approach frames identifiability as an existence and uniqueness problem in a closely related elliptic equation and draws, for homogeneous equations, on the well-known Fredholm alternative to establish unconditional identifiability, and cases where specific choices of initial and boundary conditions lead to non-identifiability. While in some sense pathological, we demonstrate that this loss of structural identifiability has ramifications for practical identifiability; important particularly for spatial problems, where the initial condition is often limited by experimental constraints. For cases with nonlinear reaction terms, uniqueness of solutions to the auxiliary elliptic equation corresponds to identifiability, often leading to unconditional global identifiability under mild assumptions. We present analysis for a suite of simple scalar models with various boundary conditions that include linear (exponential) and nonlinear (logistic) source terms, and a special case of a two-species cell motility model. We conclude by discussing how this new perspective enables well-developed analysis tools to advance the developing theory underlying structural identifiability of partial differential equations.

Journal of Mathematical BiologyVol. 91(1)
The University of Melbourne (AU), University of Oxford (GB), Mathematical Institute of the Slovak Academy of Sciences (SK)
University of Oxford, Natural Sciences and Engineering Research Council of Canada, Mathematical Institute, University of Oxford
Openalex Percentile: Top 4%
Mathematical Biology Tumor Growth
5.47
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