Stochastic simulation of pattern formation in growing tissue: a multilevel approach

TL;DR

This paper develops a multilevel stochastic simulation framework for modeling pattern formation in growing tissues, integrating single-cell reaction-diffusion models with population-level interactions, especially focusing on Notch-Delta signaling pathways.

Contribution

It introduces a novel multilevel approach to efficiently simulate large interacting cell populations with stochastic reaction-diffusion models, bridging single-cell and tissue-level dynamics.

Findings

Successful coupling of single-cell and population-level stochastic models

Efficient simulation of pattern formation in growing tissue

Insights into Notch-Delta signaling mechanisms in tissue development

Abstract

We take up the challenge of designing realistic computational models of large interacting cell populations. The goal is essentially to bring Gillespie's celebrated stochastic methodology to the level of an interacting population of cells. Specifically, we are interested in how the gold standard of single cell computational modeling, here taken to be spatial stochastic reaction-diffusion models, may be efficiently coupled with a similar approach at the cell population level. Concretely, we target a recently proposed set of pathways for pattern formation involving Notch-Delta signaling mechanisms. These involve cell-to-cell communication as mediated both via direct membrane contact sites as well as via cellular protrusions. We explain how to simulate the process in growing tissue using a multilevel approach and we discuss implications for future development of the associated computational methods.