ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
Identification of approximate symmetries in biological development.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 13 citations in OpenAlex.
- A High Throughput and User-Friendly Computational Tool for Quantifying and Visualizing Symmetry Variation in Image Datasets.Bulletin of mathematical biology · 2026Article
- Characterizing symmetry transitions in systems with dynamic morphology.Mathematical biosciences · 2025Article
- Widespread biochemical reaction networks enable Turing patterns without imposed feedback.Nature communications · 2024Article
- Inferring local molecular dynamics from the global actin network structure: A case study of 2D synthetic branching actin networks.Journal of theoretical biology · 2023Article
- Introduction to 'Recent progress and open frontiers in Turing's theory of morphogenesis'.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
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Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Virtually all forms of life, from single-cell eukaryotes to complex, highly differentiated multicellular organisms, exhibit a property referred to as symmetry. However, precise measures of symmetry are often difficult to formulate and apply in a meaningful way to biological systems, where symmetries and asymmetries can be dynamic and transient, or be visually apparent but not reliably quantifiable using standard measures from mathematics and physics. Here, we present and illustrate a novel measure that draws on concepts from information theory to quantify the degree of symmetry, enabling the identification of approximate symmetries that may be present in a pattern or a biological image. We apply the measure to rotation, reflection and translation symmetries in patterns produced by a Turing model, as well as natural objects (algae, flowers and leaves). This method of symmetry quantification is unbiased and rigorous, and requires minimal manual processing compared to alternative measures. The proposed method is therefore a useful tool for comparison and identification of symmetries in biological systems, with potential future applications to symmetries that arise during development, as observed
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