ArticlePLoS computational biology2020
Tracking collective cell motion by topological data analysis.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 28 citations in OpenAlex.
- Deep learning-based high-information-content graph representation of early stage bacterial biofilms.NPJ biofilms and microbiomes · 2026Article
- Topological data analysis of pattern formation of human induced pluripotent stem cell colonies.Scientific reports · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- Dissecting glial scar formation by spatial point pattern and topological data analysis.Scientific reports · 2024Article
- Free and Interfacial Boundaries in Individual-Based Models of Multicellular Biological systems.Bulletin of mathematical biology · 2023Article
- Anomalous Angiogenesis in Retina.Biomedicines · 2021Article
- Noise robustness of persistent homology on greyscale images, across filtrations and signatures.PloS one · 2021Article
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By modifying and calibrating an active vertex model to experiments, we have simulated numerically a confluent cellular monolayer spreading on an empty space and the collision of two monolayers of different cells in an antagonistic migration assay. Cells are subject to inertial forces and to active forces that try to align their velocities with those of neighboring ones. In agreement with experiments in the literature, the spreading test exhibits formation of fingers in the moving interfaces, there appear swirls in the velocity field, and the polar order parameter and the correlation and swirl lengths increase with time. Numerical simulations show that cells inside the tissue have smaller area than those at the interface, which has been observed in recent experiments. In the antagonistic migration assay, a population of fluidlike Ras cells invades a population of wild type solidlike cells having shape parameters above and below the geometric critical value, respectively. Cell mixing or segregation depends on the junction tensions between different cells. We reproduce the experimentally observed antagonistic migration assays by assuming that a fraction of cells favor mixing, the others segregation, and that these cells are randomly distributed in space. To characterize and compare the structure of interfaces between cell types or of interfaces of spreading cellular monolayers in an automatic manner, we apply topological data analysis to experimental data and to results of our numerical simulations. We use time series of data generated by numerical simulations to automatically group, track and classify the advancing interfaces of cellular aggregates by means of bottleneck or Wasserstein distances of persistent homologies. These techniques of topological data analysis are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant for tissue engineering, biological effects of materials, tissue and organ regeneration.
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Registered trials
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