Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.713887
Title: Mathematical modelling of cytokine dynamics in arthritic disease
Author: Baker, Michelle
Awarding Body: University of Nottingham
Current Institution: University of Nottingham
Date of Award: 2015
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Abstract:
Arthritic diseases, a group of degenerative joint diseases, cause pain, disability and the loss of independence. Research over the last 30 years has improved our understanding of these conditions. We now know that these conditions are pathological in nature, and are mediated by cytokines, cell signalling proteins. We still have much to learn about disease initiation, control and progression if we wish to develop reliable and effective disease-modifying treatments. In this thesis we use mathematical modelling to extend our understanding of arthritic disease. We focus our attention on two arthritic diseases, rheumatoid arthritis (RA), predominantly initiated in the synovium of joints, and osteoarthritis (OA), predominantly initiated in the cartilage of joints. We develop an ODE model of cytokine dynamics in the synovium and show that it contains some features associated with RA. We find that increases in cytokine production rates over time can lead to initiation of RA, including periods of relapsing-remitting disease. We find that dose timing and interval as well as dose size are all important to treatment outcome. We develop two models of cytokine dynamics in cartilage and use these to analyse OA initiation and progression. The first model is an ODE model, expanding on the synovium model, and the second model is a spatial Cellular Potts model. We use these to consider pathways that could lead to the development of OA, and show that combined treatment strategies are more effective than single target therapies in treating OA. We also show that diffusion in cartilage plays an important role in OA. We look briefly at the downstream signalling pathways of cytokines, which are also not fully understood. Here we focus on the binding of a family of transcription factors (STAT proteins) to DNA. We find that multiple high affinity binding sites are not a requirement for cooperative binding of STAT proteins.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.713887  DOI: Not available
Keywords: QA Mathematics ; QP Physiology
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