Learning the Rules of Collective Cell Migration

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Collective cell migration is important for a plethora of biological processes, including cancer metastasis, organogenesis, and wound healing. However, the biological mechanisms underpinning collective cell movement are still poorly understood. It is, therefore, desirable to better understand the mechanisms of collective migration, such that therapies for diseases resulting from misdirected or uncontrolled migration can be prevented. In this project, in vivo cell migration data from chick cranial neural crest will be used in tandem with high throughput in vitro epithelial cell wound healing data to probe the biological mechanisms underpinning collective cell migration. Efforts will be focussed on the formulation and simulation of agent-based mathematical models of migration, the outputs of which can be compared with available data to elucidate the mechanisms responsible for in vivo observations and constrain the associated biological parameters. These models can subsequently be coarse-grained to form continuum equivalents that are amenable to detailed mathematical analysis. A particular focus will be placed on the prevailing theory of leader-follower dynamics in the cranial neural crest due to the vast body of experimental evidence supporting this hypothesis and the findings of prior mathematical models adopting this framework. This work will aim to develop the findings of previous models by considering additional interaction mechanisms observed in vivo that are poorly understood, such as extracellular matrix degradation, spatially non-uniform growth of the migratory domain, and cell confinement from factors expressed within the neural crest environment. Work concerning the neural crest will be complemented by parameter estimation and model validation utilising data from experimentally tractable wound healing assays to produce models of collective migration in which parameters are known to a high degree of accuracy, further increasing the predictive capacity of models.