Interactions and dynamics in collective cell behaviour

Thesis

Collective cell behaviours, from tissue growth and collision to adhesion-driven sorting, underpin key processes in development, regeneration and disease. Yet building models that are both mechanistically accurate and quantitatively calibrated to high-resolution data remains a major challenge. In this thesis, we tackle three interlocking challenges: deriving minimal but predictive continuum and discrete models from basic principles; integrating these models with diverse experimental assays via rigorous inference; and analysing their emergent dynamics to uncover the mechanisms driving collective phenomena.

We begin with the classical Fisher–KPP and Porous–Fisher equations for tissue spreading, which we combine with a Bayesian inference framework to infer motility and proliferation parameters from epithelial monolayer assays. We then extend this picture by incorporating cell cycle data into a two-stage continuum model, revealing how crowding delays cell cycle progression and shapes proliferation patterns in expanding tissues. Extending to tissue–tissue interactions, we show that only models accounting for population pressure can reproduce the sharp interfaces observed in monolayer collision experiments. We then move to the study of adhesive interactions between cells. To bring classical nonlocal adhesion models closer to quantitative data, we derive a local fourth-order model for cell–cell adhesion in the short-range interaction limit, and show that it preserves the full phenomenology of sorting patterns in the context of differential adhesion. Finally, we build and analyse an agent-based spheroid model to explain emergent collective orbiting driven by boundary curvature, cell–cell, and cell–matrix adhesion. Together, these advances pave the way for the next generation of quantitative biology, where mechanistic modelling and data-driven methods combine to yield predictive, experimentally grounded frameworks.

Authors: Falco I Gandia, C

• DOI: 10.5287/ora-vjzdegyyr
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford
• Language: English
• Keywords: Bayesian inference; continuum models; cell interactions; cell migration; collective behaviour
• Subjects: Biology--Mathematical models