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Modelling biofilm formation

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  • Full or part time
    Prof Hoyle
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

Biofilms are communities of bacteria and other microorganisms that stick to each other and onto surfaces, so that they form a slimy layer like the black slime you sometimes get in taps and garden hoses. They can have important implications for health in some circumstances, for example by causing disease or by fouling medical implants. Dental plaque is a biofilm that forms on teeth, and Legionnaires’ disease is caused by Legionella bacteria that can grow in biofilms in air-conditioning systems. Biofilm formation is often modelled using individual-based simulations, as this allows detailed knowledge of formation mechanisms to be included, but it is very slow for macroscopic biofilms owing to the large number of cells involved (a typical cell density in a biofilm might be 10^11/ml and the many interactions between them. Current individual-based models can only simulate lengthscales of a few millimetres at best and over comparatively short timescales. This project is aimed at developing efficient algorithms for aggregating the microscopic features of individual-based biofilm models into system-level models that can predict features of the biofilm on a macroscopic scale.

Funding Notes

Prospective candidates are required to apply for this studentship by applying for the MPhil/PhD in Mathematics using the University of Southampton on-line application system. A copy of the on-line application form and guidance notes can be found at the following website: http://www.soton.ac.uk/postgraduate/pgstudy/howdoiapplypg.html

The studentship will cover tuition fees for UK/EU applicants and also provide a stipend for UK applicants.

Related Subjects

How good is research at University of Southampton in Mathematical Sciences?

FTE Category A staff submitted: 54.80

Research output data provided by the Research Excellence Framework (REF)

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