Collective chemotaxis: how cells work together to migrate more efficiently

Lead Research Organisation: University of Edinburgh
Department Name: Sch of Mathematics

Abstract

Collective movement is a widespread phenomenon in biology. Examples range from flocking birds and school of fish on a larger scale to swarming bees and ant colonies on a smaller scale. The most crucial collective behaviours for human health, however, occur on a much smaller scale:
Collective cell movement is essential for numerous cellular mechanisms, e.g. in wound healing, cancer migration and embryonic development. In the latter, so-called neural crest cells (NCC) migrate in clusters from the neural tube (prestage of the spine) throughout the whole embryo. This behaviour is necessary for the formation of neurons, glia, bone, tissue, just to name a few. A failure of this migration might lead to birth defects, such as a cleft lip. Once, this phenomenon is understood, the results could potentially help to understand cancer migration.
One of the main drives for this phenomenon is chemotaxis - the guided movement of cells towards the gradient of a chemical substance. The other two mechanisms that enable the formation of clusters and the dispersion of cells throughout the embryo are the co-attraction of cells and the contact inhibition of locomotion, respectively. Previously, this behaviour has been studied in agent-based models, however, these models are difficult to analyse and computationally expensive to simulate.
The aim of this project is to derive a macroscopic, continuous representation of this behaviour, starting from a microscopic model for one cell, a (position jump) random walk that incorporates the three mechanisms mentioned. Incorporating these three mechanisms in a biologically relevant way requires a comprehensive understanding of the underlying biological mechanisms. The macroscopic model obtained will have the form of a system of diffusion-advection-(reaction) partial differential equations (PDEs) and is comparably easy to analyse and implement.
Furthermore, we will analyse the PDE model and compare simulation results from both the macroscopic and the microscopic model.
After having done this, we will consider another modelling approach for the microscopic scale, again derive a macroscopic representation, and compare that to the previously obtained result. The exact approach we will use here is yet to be determined.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023291/1 01/10/2019 31/03/2028
2284962 Studentship EP/S023291/1 01/09/2019 31/08/2023 Viktoria Freingruber