MIBTP students undertake a period of training during their first year. This includes compulsory taught modules in statistics, programming, data analysis, AI and mini research projects.

All cells have the ability to deal with potentially lethal DNA damage caused by exposure to radiation. This cellular process helps prevent mutations being inherited and requires a protein called ATR. ATR reacts to damaged DNA by stopping it from replicating. Mutations in the ATR gene, or genes that help ATR to become switched on following the generation of DNA damage can cause disorders in man with severe developmental defects (e.g. Seckel syndrome, primary microcephaly and primordial dwarfism). Therefore, understanding how ATR recognises DNA damage and stops cell growth to allow time for the damage to be repaired is essential to understand how defects in this process contribute to human disease. Our laboratory has recently identified a protein called HNRPUL1 that we have shown is required for ATR to respond properly to some forms of DNA damage. Very little is known about the role of HNRPUL1 in the cell. Therefore, our research aim is to investigate how HNRPUL1 is involved in helping ATR function. This will be carried out in a number of ways: 1). Does HNRPUL1 help the ATR protein to be activated by helping it to bind to damaged DNA? 2). Does HNRPUL1 help other proteins, required for ATR activation, bind to ATR once it has recognised DNA damage? 3). Does HNRPUL1 help cells repair DNA damage? and 4). How does HNRPUL1 help ATR to stop cell growth to allow time for the damaged DNA to be repaired? Answering these questions will increase our understanding of the cellular response to DNA damage and may reveal the involvement of the HNRPUL1 gene in the development of human disease.

We aim to investigate new advances and expertise in machine learning to improve the reliability of disease prediction from genomic and proteomic data, and to enable answering novel biological questions regarding cell-cell communication mechanisms that underlie the development of disease. Statistical machine learning methods have already been shown to hold a lot of promise towards these goals in principle. However, high-throughput technologies result in increasingly high dimensional data, while the number of samples remains limited. The implications of these extreme conditions are largely overlooked by the existing state of the art. In addition, new biological questions are being asked that currently existing techniques are unable to tackle. Recent results in machine learning make it possible to address these issues. In particular, hybridizing generative and discriminative models may blend the benefits of both and may reduce the required sample size. An informed choice of distance functions and data models may mitigate the curse of dimensionality. Adapting certain techniques previously developed for social network inference may provide the required modelling power for inferring cell-cell communication mechanisms. By exploring the potential of these techniques, we hope to pave the way towards creating novel and improved computational methods for life scientists.

Harmonic Analysis

Quantum Spin Dynamics in Magnets