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Title: Image-based modelling of cell reorientation
Author: Lockley, Robert
ISNI:       0000 0004 6062 3816
Awarding Body: University of Warwick
Current Institution: University of Warwick
Date of Award: 2015
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Directed cell motility plays a key role in many areas of biology, with cells able to reorient quickly in response to changes of an extracellular stimuli. A complex signalling network directs this response, which motivates the use of conceptual mathematical models that replicate aspects of this behaviour and can be more readily analysed. Comparisons between such models have more focused on the qualitative differences between them. We wished to construct a framework for the rigorous comparison between models, using cell repolarisation in response to shear ow change. We fitted three reaction-diffusion models of cell polarity to experimental data of dictyostelium amoeba repolarising in response to mechanical shear flow. Experiments performed under different conditions were fitted simultaneously, to provide models with a range of cellular dynamics, with the models being fit to spatio-temporal data of cortex fluorescence of an F-actin reporter. All models were able to give a satisfactory t, with parameter identifiability determined using the pro le likelihood. The Meinhardt and Levchenko models were able to obtain better fits than the Otsuji model. Analysis of the model behaviour and parameter identifiability prompted alterations of the models, which resulted in a fully identi able two-variable Meinhardt model. Simulations of the Meinhardt and Levchenko models were used to test their behaviour over time frames past which the models had been tfitted. This motivated changes to the model parameters to obtain the desired long-term behaviour. Further simulations were run to elicit the model response to a changing external signal beyond that seen in the fitting, with the models being able to adapt to a moving signal, and respond to multiple simultaneous signals. Further fitting of the Meinhardt and Levchenko models was conducted using single cell data. The models were able to t well to data taken from both repolarising and unstimulated cells, showing that these models are able to replicate both mean and single cell spatio-temporal imaging data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: QH301 Biology