PhD in mathematical biology, fully funded for UK applicants, 3.5 years, September 2024 start
Developing computational methods to understand a fundamental cell biological process
Glycosylation is a crucial process that governs a large number of biological functions. The accurate control of glycosylation has wide reaching consequences; from ensuring biological drugs are effective and well targeted, to managing a variety of disease states, including cancer. Glycosylation occurs within a cellular component called the Golgi Body. Glycosylation enzymes sequentially modify sugar chains called glycans in the Golgi Body, until a mature glycan chain is formed. This leads to a highly heterogeneous mixture of glycans which is important for biological function.
To capture the heterogeneous mix the construction process is best simulated using a stochastic modelling approach, enabling us to integrate the complexity and diversity of the underlying processes in the model. This approach is based on the well known Gillespie algorithm for stochastic processes, yet this does not yield results of sufficient quality for biotechnological application. The goal in this project is to extend beyond the limitations of the current approach to permit the level of control required for the engineering of the underlying biology.
An essential part of our current approach is the fitting of simulation results to the ensemble of glycans emerging from the biological system in different contexts. We perform this task via Approximate Bayesian Computation, a relatively modern technique which allows us to numerically compare the output from the stochastic simulation algorithm to refine hypotheses. Further refinement and development of this technique will permit us to interrogate a wider range of biological scenarios.
The project is in collaboration with the company Ludger Ltd, a World leader in glycan analytics. This collaboration will provide useful biological data for the project in addition to the prospect to learn first hand about the generation and processing of the used biological data in the form of a placement at Ludger’s laboratories near Oxford. There will also be an opportunity to contribute new code to Ludger’s data processing pipeline.
Open to students from Students intending to enrol on the PhD in Mathematics only.
You should hold, or expect to hold, an undergraduate degree in which mathematics has formed a substantial part of the course, with a 2:1 or first-class honours (or overseas equivalent). Assessment of your application will include consideration of your full academic record, including progress on courses you are still studying.
Application deadline: Monday 22 July 2024, 12am BST
Please apply by completing the online form at https://www.york.ac.uk/study/
You should put 'Developing computational methods to understand a fundamental cell biological process' as the proposed title of your research and state the names of Professor Jamie Wood and Professor Dani Ungar as your intended supervisors. Please complete the application form as fully as you can and upload transcript(s) of current or previous university-level studies as supporting documents with your application. You should include the names and full contact details of two academic referees on your application form, with their email addresses in the correct field.
The applicant will be a Mathematically and Computationally competent student with an interest in applying their skills to problems in cell biology and biotechnology. The simulations are based in a mixture of Java and Python, so competence with one or both of these, or related languages, will be important. Knowledge of the relevant cell biology and glycobiology are not required as we expect the candidate to be keen to pick up this knowledge during the PhD. Training in related areas of mathematics will be a component of this PhD. Please enquire with dani.ungar
The project supervisors, Chair of the Graduate Research School Committee in Mathematics and administrative staff in the Department of Mathematics involved in the processing of applications for Postgraduate Research courses will be able to see the details of the applications and have access to personal information.