Spatial self-organization in hybrid models of multicellular adhesion

TL;DR

This paper introduces a new class of models for spatial self-organization in multicellular systems based on cell adhesion and phenotypic switching, demonstrating pattern formation without reaction-diffusion mechanisms.

Contribution

The study presents a minimal stochastic model of cell adhesion and phenotypic switching that can generate ordered spatial patterns, expanding understanding of pattern formation beyond reaction-diffusion frameworks.

Findings

Pattern-forming rules depend on adhesion and switching probabilities.

Ordered spatial patterns emerge without reaction-diffusion processes.

Certain organizational phases lead to higher population levels.

Abstract

Spatial self-organization emerges in distributed systems exhibiting local interactions when nonlinearities and the appropriate propagation of signals are at work. These kinds of phenomena can be modeled with different frameworks, typically cellular automata or reaction-diffusion systems. A different class of dynamical processes involves the correlated movement of agents over space, which can be mediated through chemotactic movement or minimization of cell-cell interaction energy. A classic example of the latter is given by the formation of spatially segregated assemblies when cells display differential adhesion. Here we consider a new class of dynamical models, involving cell adhesion among two stochastically exchangeable cell states as a minimal model capable of exhibiting well-defined, ordered spatial patterns. Our results suggest that a whole space of pattern-forming rules is hosted by the combination of physical differential adhesion and the value of probabilities modulating cell phenotypic switching, showing that Turing-like patterns can be obtained without resorting to reaction-diffusion processes. If the model is expanded allowing cells to proliferate and die in an environment where diffusible nutrient and toxic waste are at play, different phases are observed, characterized by regularly spaced patterns. The analysis of the parameter space reveals that certain phases reach higher population levels than other modes of organization. A detailed exploration of the mean-field theory is also presented. Finally we let populations of cells with different adhesion matrices compete for reproduction, showing that, in our model, structural organization can improve the fitness of a given cell population. The implications of these results for ecological and evolutionary models of pattern formation and the emergence of multicellularity are outlined.