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Title: Physical modelling of epithelia : reverse engineering cell competition in silico
Author: Gradeci, Daniel
ISNI:       0000 0004 8508 0336
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Cell competition is a phenomenon in which less fit cells are removed from a tissue for optimal survival of the host. Competition has been observed in many physiological and pathophysiological conditions, especially in the prevention of tumor development. While there have been extensive population-scale experimental studies of competition, the competitive strategies and their underlying mechanisms in single cells are poorly understood. To date, two main mechanisms of cell competition have been described. Mechanical competition arises when the two competing cell types have different sensitivities to crowding. In contrast, during biochemical competition, signaling occurs at the interface between cell types leading to apoptosis of the loser cells. However, rigorously testing these hypotheses remains challenging due to the difficulty of obtaining sufficient single cell level information to bridge scales to the whole tissue. In this thesis, I present metrics aimed at characterising competition at the single cell level. Then, I demonstrate the development of a multi-layered, cell-scale computational model that I use to gain understanding on the single cell mechanisms that govern mechanical competition and decipher the "rules of the cellular game". After benchmarking cell growth and homeostasis in pure populations, I show that competition emerges when both cell types are included in simulations. I then investigate the impact of each computational parameter on the outcome of cell competition. Intriguingly, the outcome of biochemical competition is controlled by topological entropy between cell types, whereas the outcome of mechanical cell competition is exclusively controlled by differences in energetic potential between cell types. As 90% of cancers arise from epithelia and a number of genetic diseases present symptoms of epithelial fragility, I anticipate that my model of realistic implementation of epithelia will be of use to the biophysics and computational modelling community.
Supervisor: Charras, G. ; Lowe, A. ; Banerjee, S. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available