Quantification of cellular contributions to multi-cellular pattern formation
- 1Cenetr for Theoretical Physics (CPT), French National Center for Scientific Research (CNRS)
Abstract
Multi-cellular pattern formation is a key process in organism development, regeneration, and disease. Such multi-cellular patterns include, for instance, cell density and gene expression patterns. Here, we develop a cell-track-based method that quantifies how much individual cellular processes contribute to the emergence of large-scale patterns. These cellular processes include changes in cellular expression levels, cell motion, cell divisions, and apoptosis. We validate our method using synthetic data, before demonstrating it on two sets of experimental data. Our method is purely geometric and does not require any mechanistic assumptions about the underlying pattern-forming processes, making it very generic and robust. It thus enables unbiased, data-driven study of the mechanisms driving pattern formation across a broad class of systems.
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BibTeX
@misc{estavoyer2026quantification,
title = {{Quantification of cellular contributions to multi-cellular pattern formation}},
author = {M. Estavoyer and M. L. Zhao and M. Merkel},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.08.757240},
url = {https://www.biorxiv.org/content/10.64898/2026.10.08.757240v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Quantification of cellular contributions to multi-cellular pattern formation
AU - M. Estavoyer
AU - M. L. Zhao
AU - M. Merkel
PY - 2026
DA - 2026/10/08
PB - bioRxiv
DO - 10.64898/2026.10.08.757240
UR - https://www.biorxiv.org/content/10.64898/2026.10.08.757240v1
AB - Multi-cellular pattern formation is a key process in organism development, regeneration, and disease. Such multi-cellular patterns include, for instance, cell density and gene expression patterns. Here, we develop a cell-track-based method that quantifies how much individual cellular processes contribute to the emergence of large-scale patterns. These cellular processes include changes in cellular expression levels, cell motion, cell divisions, and apoptosis. We validate our method using synthetic data, before demonstrating it on two sets of experimental data. Our method is purely geometric and does not require any mechanistic assumptions about the underlying pattern-forming processes, making it very generic and robust. It thus enables unbiased, data-driven study of the mechanisms driving pattern formation across a broad class of systems.
ER -