Please contact me by email if you would like to informally discuss any of these projects.
Projects with funding
Projects will be advertised here when available.
Unfunded projects
Applicants interested in these projects would need to already have a scholarship in place, or successfully apply for funding before they could commence their studies.
Supervisor:
D
Walker, Department of Computer Science
Myeloma is an almost invariably incurable cancer of the bone marrow. The disease usually responds well to chemotherapy and enters a remission phase.
During this phase, residual myeloma cells are thought to reside in specialist niche environments within the bone marrow which can nurture and protect
dormant cells but subsequently stimulate these cells to rapidly grow, divide and lead to re-accumulation and relapse.
Understanding the complex interplay between the cells, tissue and bone is essential for developing potential therapies
to prevent relapse and improve patient survival. However, it is difficult to observe dynamic behaviour in the ‘niche’ in vivo,
but emergent system behaviour and “what-if” scenarios can be explored using computational models.
This project would involve the development of an agent-based model representing the myeloma niche, informed by biological data generated by biologists and clinicians working in the field. The model would be implemented using our highly optimised FLAME GPU simulation environment, and the efficiency further improved by the application of state of the art machine learning and model reduction techniques.
The ideal candidate would have a background in Computer Science or a related subject with programming expertise, but an interest in understanding biological systems. Experience in GPU programming would be an advantage.
This project would involve the development of an agent-based model representing the myeloma niche, informed by biological data generated by biologists and clinicians working in the field. The model would be implemented using our highly optimised FLAME GPU simulation environment, and the efficiency further improved by the application of state of the art machine learning and model reduction techniques.
The ideal candidate would have a background in Computer Science or a related subject with programming expertise, but an interest in understanding biological systems. Experience in GPU programming would be an advantage.
Supervisor:
D
Walker, Department of Computer Science
The oviduct, or fallopian tube,
connects the ovary to the womb (uterus) and is the
site of fertilisation in mammals, leading to growth of
an embryo. However, the oviduct is not a simple tube,
but has a complex 3D structure, with internal tissue
folding which changes along the length of the organ.
This tissue surface consists of a layer of cells which
vary considerably in terms of their surface
properties. The interaction of sperm and oocyte with
this complex structure is thought to be critical in
determining whether fertilisation takes place.
This project would involve building on our previous work, which has developed simple agent-based models of sperm oviduct interactions within a virtual environment in the form of a three dimensional graphical representation of the complex oviduct structure. You would extend this agent-based model developed in our existing FLAME-GPU based framework to include additional biological interactions based on experimental data, in order to create a multiscale model of this system that will allow us to understand the role of sperm-oviduct interactions in influencing the fertilisation process.
The ideal candidate would have a background in Computer Science or a related subject with programming expertise, but an interest in understanding biological systems. Experience in GPU programming would be an advantage.
This project would involve building on our previous work, which has developed simple agent-based models of sperm oviduct interactions within a virtual environment in the form of a three dimensional graphical representation of the complex oviduct structure. You would extend this agent-based model developed in our existing FLAME-GPU based framework to include additional biological interactions based on experimental data, in order to create a multiscale model of this system that will allow us to understand the role of sperm-oviduct interactions in influencing the fertilisation process.
The ideal candidate would have a background in Computer Science or a related subject with programming expertise, but an interest in understanding biological systems. Experience in GPU programming would be an advantage.