Evidence map›Paper›PMID 41442067›Full record

ArticleBulletin of mathematical biology2025

Persistent Homology Classifies Parameter Dependence of Patterns in Turing Systems.

Reemon Spector, Heather A Harrington, Eamonn A Gaffney

Abstract read
In one paragraph

Article in Bulletin of mathematical biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Reemon SpectorMathematical Institute, University of Oxford, England, United Kingdom.
Heather A HarringtonMathematical Institute, University of Oxford, England, United Kingdom.
Eamonn A GaffneyMathematical Institute, University of Oxford, England, United Kingdom. gaffney@maths.ox.ac.uk.

Funding

Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
6 · The paper itself

Abstract

This paper illustrates a further application of topological data analysis to the study of self-organising models for chemical and biological systems. In particular, we investigate whether topological summaries can capture the parameter dependence of pattern topology in reaction diffusion systems, by examining the homology of sublevel sets of solutions to Turing reaction diffusion systems for a range of parameters. We demonstrate that a topological clustering algorithm can reveal how pattern topology depends on parameters, using the chlorite-iodide-malonic acid system, and the prototypical Schnakenberg system for illustration. In addition, we discuss the prospective application of such clustering, for instance in refining priors for detailed parameter estimation for self-organising systems.

Indexed as

Models, BiologicalModels, ChemicalAlgorithmsCluster AnalysisComputer SimulationDiffusionMalonatesMathematical ConceptsSystems BiologyMalonatesmalonic acid

Identifiers

PMID41442067
PMCPMC12738652

What OpenQuestion holds

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Registered trials

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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.