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Multiple scale modelling of skeletal muscle bioenergetics with applications to cystic fibrosis. Mathematics, PhD (GW4 BioMed MRC DTP)

  • Full or part time
  • Application Deadline
    Friday, November 23, 2018
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Supervisory team:
Dr Kyle Wedgwood, Department of Mathematics, Living Systems Institute, University of Exeter
Dr James Betts, University of Bath
Professor Craig Williams, Department of Sport and Health Sciences, University of Exeter

This project will develop and analyse a mathematical model of muscle metabolism and energetics during exercise. The model will link the energy demands of electrical activity associated with muscle contraction at the single-cell level, with those at the tissue level. The model will be used to explore aerobic fitness in patients with cystic fibrosis.

Despite significant advances in knowledge of muscle and exercise physiology, it remains difficult to make quantifiable predictions about aerobic fitness, even in healthy groups. This is due, in part, to the lack of tools to integrate information about these systems from different sources and across different spatial and temporal scales. Mathematical modelling has the potential to bridge these scales in a quantifiable, verifiable and predictive manner. This project will develop and analyse a mechanistic mathematical model of skeletal muscle bioenergetics, describing adenosine triphopshate (ATP) turnover during muscle contraction and metabolism.

Once formed, the model will be used to estimate whole-body energetic parameters via fitting to magnetic resonance spectroscopy data collected during aerobic exercise. These parameters will then be used to provide a quantitative link between calorimetry measurements of oxygen uptake during exercise to rates of aerobic and anaerobic metabolism at the tissue level. In so doing, the model will bridge scales between single cell, tissue and whole-body dynamics. Finally, the model will be used to explore aberrations in muscle metabolism and contractility associated with cystic fibrosis. Importantly, the model will investigate how exercise therapy can mitigate these aberrations.

This interdisciplinary project leverages large quantities of unique physiological data obtained at the Universities of Exeter and Bath, and our international partners (UNAM-National Autonomous University of Mexico & University of Toronto). These will be used to support state-of-the-art techniques in parameter optimisation and uncertainty quantification to capture variation in parameters across populations. The project thus involves significant amounts of data analysis, in conjunction with the construction of multiscale mathematical models. The fields of uncertainty quantification and multiscale modelling are at the forefront of mathematical biology research, and this project will therefore advance both understanding of muscle physiology and applied mathematics.

Given the importance in measuring and improving aerobic fitness in people with cystic fibrosis, there are clear avenues for translating the proposed research. In particular, the student will have direct access to patient data collected throughout the project via Williams’ funded 4 year Strategic Research Centre award by the Cystic Fibrosis Trust UK. These data and the model will be combined to predict how changes to aerobic fitness may be realised during therapy. This project will build on previous research by the supervisory team (Wellcome Trust ISSF funding 2015-6) and direct future planned projects will be a long-term aim of developing model-based decision support software for clinicians treating people with cystic fibrosis.

To apply for this project please complete the application form at by 5pm Friday 23 November 2018.

Funding Notes

This studentship is funded through GW4 BioMed MRC Doctoral Training Partnership. It consists of full UK/EU tuition fees, as well as a Doctoral Stipend matching UK Research Council National Minimum (£14,777 for 2018/19, updated each year) for 3.5 years.

For further information relating to the funding please see: View Website

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