Evidence map›Paper›PMID 40439754›Full record

ArticleJournal of mathematical biology2025

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

Yurij Salmaniw, Alexander P Browning

Abstract read
In one paragraph

Article in Journal of mathematical biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yurij SalmaniwMathematical Institute, University of Oxford, Oxford, United Kingdom. salmaniw@maths.ox.ac.uk.ORCID http://orcid.org/0000-0001-7315-755X
Alexander P BrowningMathematical Institute, University of Oxford, Oxford, United Kingdom.ORCID http://orcid.org/0000-0002-8753-1538

Funding

Mathematical Institute, University of Oxford Hooke Research FellowshipNatural Sciences and Engineering Research Council of Canada PDF-578181-2023
6 · The paper itself

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.

Indexed as

Models, BiologicalAnimalsCell MovementComputer SimulationHumansLinear ModelsMathematical ConceptsNonlinear DynamicsDistinguishabilityFredholm alternativePartial differential equationsSpectral theory of compact operatorsStructural identifiability

Identifiers

PMID40439754
PMCPMC12122637

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.