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Modelling of cellular adhesion in close collaboration with experimental biologists

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  • Full or part time
    Dr L Chumakova
  • Application Deadline
    Applications accepted all year round
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

All the multicellular organisms, including us, humans, are in the end just a pile of cells. But why we are not falling apart? Our team studies cell-cell adhesion from an intracellular perspective. This is a vibrant current area of research, where most labs currently study tissue properties from the macroscopic level (tissue tension, etc), but not from the intracellular level. For cells to stick together, a particular protein must be delivered to its biologically relevant locations along the cell boundary (in order to "glue" nearby cells together) and distributed in such a way that the tissue has desired biological properties. How does this work? Is the outcome robust? We study these processes in close collaboration with the experimental biology lab of Natalia Bulgakova at U. Sheffield, with maths modelling guiding the experiments and vice versa. Maths techniques that we use include PDEs, ODEs, and Stochastic modelling. No previous knowledge of biology is required at the beginning of the project, but the student will learn about the relevant bio processes as the project progresses. The results of this highly interdisciplinary project will be of interest to both maths and bio communities. Informal enquiries can be made to Lyuba Chumakova ([email protected]).

Funding Notes

Funding is available through competitive scholarships; http://www.maths.ed.ac.uk/school-of-mathematics/studying-here/pgr/funding-opportunities for details. To be considered for these, applicants need to meet the following deadline: 1st December 2017 for early admission, and 31 January 2018 for standard admission.

How good is research at University of Edinburgh in Mathematical Sciences?
(joint submission with Heriot-Watt University)

FTE Category A staff submitted: 56.80

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

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