Student research project
Supervisor(s): David Ascher
Driving the development of computational tools to guide personalised medicine approaches
We have developed a range of computational tools to deconvolute the molecular mechanisms of a mutation giving rise to different phenotypes. In collaboration with clinical partners we have shown that even though patients may present the same disease, they may arise from many different mutations that alter a patient’s outcome or how they may respond to a particular treatment. By analysing these mutations and predicting their effects on protein structure and function we are trying to revolutionise treatment strategies, an important step towards personalised medicine.
We are currently working on a range of diseases including genetic diseases (Alkaptonuria, urea cycle disorders, VHL), cancer (renal carcinomas), and drug/vaccine resistance (TB, cancer, malaria, HIV, influenza). These projects combine both computational (bioinformatics) and experimental (protein expression, biophysics, structural biology) approaches to unravel the molecular mechanisms driving these mutations and derive novel predictive methods. This information is then used to help identify and guide the development of novel therapies to treat these conditions. One of the ultimate goals of these projects will be the development of webservers enabling the rapid analysis of mutations to help guide clinical decisions.
Ideal for students with some familiarity with Linux operating systems and computer coding (Python).
Techniques used will include:
- protein structure analysis
- in silico mutation analysis
- machine learning and neural networks
- webserver development.