Accessibility navigation

A quantitative approach to signal processing in cancer cell dispersal

Butler, G. (2021) A quantitative approach to signal processing in cancer cell dispersal. PhD thesis, University of Reading

[img] Text - Thesis
· Restricted to Repository staff only until 28 June 2023.

[img] Text - Thesis Deposit Form
· Restricted to Repository staff only


It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.48683/1926.00106345


An important question in cancer evolution concerns which traits make a cell likely to success�fully metastasise. Through a combination of experimental evolution and computer vision a series of mathematical models have been developed throughout this thesis to investigate the individual signal processing behaviour of cancer cells during dispersal. In Chapter 2 a convolutional neural network is used to demonstrate how the morphology of individual cells can be automatically segmented within phase contrast time-lapse videos. The segmented morphologies are then used in Chapter 3 to explore the idea of signal processing mediated dispersal to reveal a density-dependent phenotype only seen in cells selected for distant site colonisation. Specifically, the model shows that the rate of morphological change is positively correlated with the speed of migration when the local cell density is high. However, when the local cell density is low the opposite relationship is displayed: the rate of morpho�logical change decreases with an increase in migration speed. Chapter 4 then builds upon the results of Chapter 3 to develops two temporally dependent morphological model that quantify short term temporal changes in dispersal dynamics at both a population and single cell level. The temporally dependent models reveal that in fact a subset of cells in all of the experimental populations can adopt similar complex behaviour. However, the populations differ in their behavioural demography as well as the frequency at which a given behaviour is adopted through time. Finally, Chapter 5 employs a similar temporally resolved approach to investigate the interaction between the broader cancer cell population and a small subset of cancer cells known as poly-aneuploid cancer cells. In summary, this thesis harnesses the power of mature mathematical techniques to investigate novel and emergent characteristics of metastatic dispersal in a quantitative and statistically robust manner.

Item Type:Thesis (PhD)
Thesis Supervisor:Dash, P. and Johnson, L.
Thesis/Report Department:School of Biological Sciences
Identification Number/DOI:
Divisions:Life Sciences > School of Biological Sciences
ID Code:106345

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation