ArticleProceedings of the National Academy of Sciences of the United States of America2020
Topological data analysis of zebrafish patterns.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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Who cites it
38 citing papers in PubMed.
- Color patterns as a model for connecting developmental mechanisms to phenotypic evolution.Nature communications · 2026Review
- Deep learning framework for quantifying self-organization inProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Persistent Homology Classifies Parameter Dependence of Patterns in Turing Systems.Bulletin of mathematical biology · 2025Article
- Topology across scales on heterogeneous cell data.PLoS computational biology · 2025Article
- Migrating mesoderm cells self-organize into a dynamic meshwork structure during chick gastrulation.eLife · 2025Article
- Topological data analysis of pattern formation of human induced pluripotent stem cell colonies.Scientific reports · 2025Article
- A new paradigm considering multicellular adhesion, repulsion and attraction represent diverse cellular tile patterns.PLoS computational biology · 2025Article
- Deep phenotypic profiling of neuroactive drugs in larval zebrafish.Nature communications · 2024Article
- Topological Approach to Void Finding Applied to the SDSS Galaxy Map.The Astrophysical journal · 2024Article
- Linking discrete and continuous models of cell birth and migration.Royal Society open science · 2024Article
- Dory: Computation of persistence diagrams up to dimension two for Vietoris-Rips filtrations of large data sets.Journal of computational science · 2024Article
- A novel multi-task machine learning classifier for rare disease patterning using cardiac strain imaging data.Scientific reports · 2024Article
- Article
- Unlocking the Potential of Zebrafish Research with Artificial Intelligence: Advancements in Tracking, Processing, and Visualization.Medical & biological engineering & computing · 2023Review
- Topological data analysis of spatial patterning in heterogeneous cell populations: clustering and sorting with varying cell-cell adhesion.NPJ systems biology and applications · 2023Article
- Zigzag persistence for coral reef resilience using a stochastic spatial model.Journal of the Royal Society, Interface · 2023Article
- The shape of cancer relapse: Topological data analysis predicts recurrence in paediatric acute lymphoblastic leukaemia.PLoS computational biology · 2023Article
- Stripes and loss of color in ball pythons (Python regius) are associated with variants affecting endothelin signaling.G3 (Bethesda, Md.) · 2023Article
- Quantitative characterization of zebrafish development based on multiple classifications using Mueller matrix OCT.Biomedical optics express · 2023Article
- Tight basis cycle representatives for persistent homology of large biological data sets.PLoS computational biology · 2023Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Self-organized pattern behavior is ubiquitous throughout nature, from fish schooling to collective cell dynamics during organism development. Qualitatively these patterns display impressive consistency, yet variability inevitably exists within pattern-forming systems on both microscopic and macroscopic scales. Quantifying variability and measuring pattern features can inform the underlying agent interactions and allow for predictive analyses. Nevertheless, current methods for analyzing patterns that arise from collective behavior capture only macroscopic features or rely on either manual inspection or smoothing algorithms that lose the underlying agent-based nature of the data. Here we introduce methods based on topological data analysis and interpretable machine learning for quantifying both agent-level features and global pattern attributes on a large scale. Because the zebrafish is a model organism for skin pattern formation, we focus specifically on analyzing its skin patterns as a means of illustrating our approach. Using a recent agent-based model, we simulate thousands of wild-type and mutant zebrafish patterns and apply our methodology to better understand pattern variability in zebrafish. Our methodology is able to quantify the differential impact of stochasticity in cell interactions on wild-type and mutant patterns, and we use our methods to predict stripe and spot statistics as a function of varying cellular communication. Our work provides an approach to automatically quantifying biological patterns and analyzing agent-based dynamics so that we can now answer critical questions in pattern formation at a much larger scale.
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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.