About the Project
Traditionally most of the experimental and modelling research focused on the analysis of the networks within individual cancer cells, treating them as isolated closed systems. However, we now know that cancer cells will behave very differently in different cellular environments. These different environments are characterised by the presence of healthy cells, including immune cells or stromal cells, but also by physical variables including mechanical resistance of the tissue, or the local oxygen concentrations. New experimental evidence shows that some healthy cells can produce nutrients that are taken up by cancer cells. Therefore, the molecular crosstalk with healthy cells can help to fuel the growth of these cancer cells.
In this project, you will develop mathematical models to uncover the crosstalk of cancer and healthy cells. The models will be initially set up with experimental data from the lab of Dr Dan Tennant (https://www.tennantlab.uk/). Then, you will make new predictions to identify key molecules that facilitate the crosstalk of these cells. This work may therefore identify new drug targets that can inhibit the crosstalk and therefore limit cancer growth, or, in the best case, reprogram cancer cells towards cell death. By working closely with the experimentalists, you will ensure the developed models are realistic and can be validated by new experiments.
This project is part of a larger-scale initiative of collaborative projects between the University of Birmingham’s School of Mathematics and the Institute of Metabolism and Systems Research that also involves Dr David Tourigny (Columbia University), Dr Vijay Rajagopal (University of Melbourne) and Dr Michael Mak (Yale University).
Methods: The project may involve several of the following methods: differential equations, metabolic flux analysis, optimisation, machine learning, agent based models, stochastic processes, image analysis, statistical analysis. Which methods are developed for this project will depend on the skills and interests of the successful candidate. Training on methods the candidate wishes to learn will be provided. The essential requirement are strong academic performance (e.g. evidenced through an excellent 1st class degree in Mathematics, Theoretical Physics, Computational Biology or related subjects), and motivation to work in an interdisciplinary environment.
funding may be available through a college or EPSRC scholarship in competition with all other PhD applications;
early applications are strongly recommended;
For non-UK/non-EU candidates:
strong applicants with external scholarships are encouraged to apply;
exceptionally strong candidates in this category may additionally be awarded a tuition fee waiver (for up to 3 years) in competition with all other PhD applications.
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