Topological data analysis of zebrafish patterns
Wiley -- Proceedings of the National Academy of Sciences
DOI 10.1073/pnas.1917763117
Keyword(s)
  1. topological data analysis
  2. agent-based model
  3. self-organization
  4. pattern quantification
  5. zebrafish
Abstract(s)

Significance

While pattern formation has been studied extensively using experiments and mathematical models, methods for quantifying self-organization are limited to manual inspection or global measures in many applications. Our work introduces a methodology for automatically quantifying patterns that arise due to agent interactions. We combine topological data analysis and machine learning to provide a collection of summary statistics describing patterns on both microscopic and macroscopic scales. We apply our methodology to study zebrafish patterns across thousands of model simulations, allowing us to make quantitative predictions about the types of pattern variability present in wild-type and mutant zebrafish. Our work helps address the widespread challenge of quantifying agent-based patterns and opens up possibilities for large-scale analysis of biological data and mathematical models.