Efficient approaches to simulating individual-based cell population models

Computational modelling of populations of cells has been applied to further understanding in a range of biological fields, from cell sorting to tumour development. The ability to analyse the emergent population-level effects of variation at the cellular and subcellular level makes it a powerful approach. As more detailed models have been proposed, the demand for computational power has increased. While developments in microchip technology continue to increase the power of individual compute units available to the research community, the use of parallel computing offers an immediate increase in available computing power. To make full use of parallel computing technology it is necessary to develop specialised algorithms. To that end, this thesis is concerned with the development, implementation and application of a novel parallel algorithm for the simulation of an off-lattice individual-based model of a population of cells. We first use the Message Passing Interface to develop a parallel algorithm for the overlapping spheres model which we implement in the Chaste software library. We draw on approaches for parallelising molecular dynamics simulations to develop a spatial decomposition approach to dividing data between processors. By using functions designed for saving and loading the state of simulations, our implementation allows for the parallel simulation of all subcellular models implemented in Chaste, as well as cell-cell interactions that depend on any of the cell state variables. Our implementation allows for faithful replication of model cells that migrate between processors during a simulation. We validate our parallel implementation by comparing results with the extensively tested serial implementation in Chaste. While the use of the Message Passing Interface means that our algorithm may be used on shared- and distributed-memory systems, we find that parallel performance is limited due to high communication costs. To address this we apply a series of optimisations that improve the scaling of our algorithm both in terms of compute time and memory consumption for given benchmark problems. To demonstrate an example application of our work to a biological problem, we extend our algorithm to enable parallel simulation of the Subcellular Element Model (S.A. Sandersius and T.J. Newman. Phys. Biol., 5:015002, 2008). By considering subcellular biomechanical heterogeneity we study the impact of a stiffer nuclear region within cells on the initiation of buckling of a compressed epithelial layer. The optimised parallel algorithm decreases computation time for a single simulation in this study by an order of magnitude, reducing computation time from over a week to a single day.This thesis is not currently available in ORA