PhD Studentship: Efficient algorithms for simulating biofilm formation.
PhD Studentship: Efficient algorithms for simulating biofilm formation.Education Hub (Engineering & the Env) Location: Highfield Campus
Closing Date: Friday 02 December 2016
Project Reference: NGCM-0065
This project aims to develop efficient algorithms for simulating the formation of biofilms, which are slimy layers ofbacteria and other microorganisms that stick to surfaces. Biofilms can have important implications for health, 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 simulationsthat capture the behavior of each individual bacterial cell, 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 and the many interactions between them. In this project we will develop algorithms to aggregate the microscopic features of individual-based biofilm models into system-level models that can predict features of biofilms on a macroscopic scale. A possible approach might be based around so-called equation-free techniques that sample the microscopic model, treating it as a black-box timestepper, in order to estimate the evolution over longer spatial and temporal scales.
If you wish to discuss any details of the project informally, please contact Prof Rebecca Hoyle, Applied Mathematics research group, Email: firstname.lastname@example.org, Tel: +44 (0) 2380 593733.
This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling (http://ngcm.soton.ac.uk). For details of our 4 Year PhD programme, please see http://www.findaphd.com/search/PhDDetails.aspx?CAID=331&LID=2652
For a details of available projects click here http://www.ngcm.soton.ac.uk/projects/index.html
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