Journal article
Persistent homology classifies parameter dependence of patterns in Turing systems
- Abstract:
- This paper illustrates a further application of topological data analysis to the study of selforganising 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.4MB, Terms of use)
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- Publisher copy:
- 10.1007/s11538-025-01552-9
Authors
- Publisher:
- Springer Nature
- Journal:
- Bulletin of Mathematical Biology More from this journal
- Volume:
- 88
- Issue:
- 1
- Article number:
- 10
- Publication date:
- 2025-12-24
- Acceptance date:
- 2025-10-14
- DOI:
- EISSN:
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1522-9602
- ISSN:
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0092-8240
- Language:
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English
- Pubs id:
-
2299789
- Local pid:
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pubs:2299789
- Deposit date:
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2025-10-14
- ARK identifier:
Terms of use
- Copyright holder:
- Spector et al
- Copyright date:
- 2025
- Rights statement:
- © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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