Postgrad LIVE! Study Fairs

Birmingham | Edinburgh | Liverpool | Sheffield | Southampton | Bristol

University of East Anglia Featured PhD Programmes
University College London Featured PhD Programmes
University of Oxford Featured PhD Programmes
Bournemouth University Featured PhD Programmes
University of Nottingham Featured PhD Programmes

Bayesian Hierarchical Models and Flexible Proteins with biomedical applications

This project is no longer listed in the FindAPhD
database and may not be available.

Click here to search the FindAPhD database
for PhD studentship opportunities
  • Full or part time
    Prof KV Mardia
  • 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

If the last century belongs to Physical Sciences, then this century must belong to Life Sciences. Many breakthroughs have been seen, starting from DNA and proteins. Their applications in drug discovery are well known but still there are many diseases needing new drug developments. A recent review of the statistical importance in this area is given in Mardia (2013 Journal of the Royal Statistical Society) .

It is well known that proteins are the work-horses of all living systems. A protein is a sequence of amino acids, of which there are twenty types. The sequence folds into a 3-dimensional structure. This three-dimensional shape of a protein plays a key role in determining its function, so proteins in which particular atoms have very similar configurations in space often have similar functions. There is therefore a need for efficient methodology to align the proteins after allowing for rigid transformations.

Real problems in Protein Bioinformatics can be very complex, such as their flexibility. This problem fits in statistical shape analysis where one assumes that the points in two or more configurations (atoms here) are labelled and these configurations are to be matched after filtering out some transformation such as a rigid transformation. Green, P.J. and Mardia, K.V. (2006, Biometrika, 93) have developed a new Bayesian hierarchical method to align unlabelled configurations under rigid transformation. This project will extend the methodology to allow for various different types of transformations, mostly motivated by problems of protein flexibility. Several medical data sets are already available to test the adequacy of the tools developed under this project.

Funding Notes

PhD project eligible for School of Mathematics EPSRC Doctoral Training Grant (DTG) Scholarship paying fees at full UK/EU rate and maintenance of £13,726pa for 3.5 years . UK applicants are eligible for a full award paying tuition fees and maintenance. EU applicants are eligible for an award paying tuition fees only. In exceptional circumstances, where residency has been established for more than 3 years prior to the start of the course, they may be eligible for a full award paying fees and maintenance.

Related Subjects

FindAPhD. Copyright 2005-2018
All rights reserved.