• FindA University Ltd Featured PhD Programmes
  • University of Manchester Featured PhD Programmes
  • Heriot-Watt University Featured PhD Programmes
  • King Abdullah University of Science and Technology (KAUST) Featured PhD Programmes
  • University of Birmingham Featured PhD Programmes
  • University College London Featured PhD Programmes
  • University of Southampton Featured PhD Programmes
University of Liverpool Featured PhD Programmes
King’s College London Featured PhD Programmes
Imperial College London Featured PhD Programmes
University of the West of England, Bristol Featured PhD Programmes
University of Surrey Featured PhD Programmes

Evolution of the protein interaction network using computational methods

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 S Lovell
    Prof D Robertson
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

Protein-protein interactions underpin almost all biological functions, with the majority of proteins making at least one interaction with another. If we are to understand how function arises in the cell, an important prerequisite is to understand how protein-protein interactions arise and evolve.

When we analyse sets of related proteins we find that they vary substantially in the sets of interactions that they make. In other words, the network of interactions “rewires” itself through evolutionary time. This rewiring can give rise to enormous complexity and emergent biological function. We propose to study this process of evolutionary rewiring using computational methods. This will involve the use of protein structure, knowledge of how proteins bind through specific interfaces, and evolutionary models. This approach differs from other techniques that attempt to define specificity and binding surfaces in that it is “function-led” (based on the interaction network and the functional annotation), rather than being “sequence-led” (based one the partitioning of the sequences using a phylogenetic tree).

The majority of the data used will be derived from the yeast Saccharomyces cerevisiae. Since Manchester is a centre for yeast research there is may be a possibility to test computational predictions in collaboration with experimental labs.

Funding Notes

This project has a Band 1 fee. Details of our different fee bands can be found on our website. For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website. Informal enquiries may be made directly to the primary supervisor.

Share this page:

Cookie Policy    X