Quantifying tissue growth, shape and collision via continuum models and Bayesian inference

Carles Falcó, Daniel J. Cohen, José A. Carrillo, Ruth E. Baker

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Although tissues are usually studied in isolation, this situation rarely occurs in biology, as cells, tissues and organs coexist and interact across scales to determine both shape and function. Here, we take a quantitative approach combining data from recent experiments, mathematical modelling and Bayesian parameter inference, to describe the self-assembly of multiple epithelial sheets by growth and collision. We use two simple and well-studied continuum models, where cells move either randomly or following population pressure gradients. After suitable calibration, both models prove to be practically identifiable, and can reproduce the main features of single tissue expansions. However, our findings reveal that whenever tissue-tissue interactions become relevant, the random motion assumption can lead to unrealistic behaviour. Under this setting, a model accounting for population pressure from different cell populations is more appropriate and shows a better agreement with experimental measurements. Finally, we discuss how tissue shape and pressure affect multi-tissue collisions. Our work thus provides a systematic approach to quantify and predict complex tissue configurations with applications in the design of tissue composites and more generally in tissue engineering.

Original languageEnglish (US)
Article number20230184
JournalJournal of the Royal Society Interface
Issue number204
StatePublished - Jul 19 2023

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biophysics
  • Biochemistry
  • Biotechnology
  • Biomedical Engineering
  • Biomaterials


  • Bayesian inference
  • cell migration
  • continuum model
  • identifiability analysis
  • population pressure


Dive into the research topics of 'Quantifying tissue growth, shape and collision via continuum models and Bayesian inference'. Together they form a unique fingerprint.

Cite this