Parameterizing models of collective cell spreading

The study of how cell populations grow and spread is integral to understanding and predicting the invasion of cancer, the speed of wound repair and the robustness of embryonic development [20, 81, 96]. However, the extent to which cell populations grow and spread is governed by multiple processes, including motility, proliferation, adhesion and death, making it difficult to elucidate the relative contributions of these processes to the growth and invasion of a cell colony [106]. As such, in vitro cell biology assays are routinely used to probe the mechanisms by which cells interact, and the key processes involved in the growth and expansion of cell colonies. These in vitro assays generally involve seeding a population of cells on a two-dimensional substrate, and observing the population as the individual cells move and proliferate and the density of the monolayer increases towards confluence. A useful approach to interpret the results of these assays involves using a mathematical model that incorporates mechanistic descriptions of processes such as cell motility and proliferation. By parameterizing and validating the models using quantitative data from in vitro assays it is possible to provide quantitative insights into the mechanisms driving the growth and spreading of a cell population, and make experimentally testable predictions. However, it is not always clear how best to choose the experimental design, nor which summary statistics of the data to collect, in order to accurately and efficiently parameterize and validate models. The importance of various experimental design choices in arriving at testable predictions and outcomes are evaluated in this thesis, for a variety of models, cell types and currently employed experimental protocols. The use of inference is demonstrated for the first time in a previously developed force-based mathematical model, extended to account for proliferation.