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Application of machine learning to establish epigenetic regulators in breast and bladder epithelial tissue


Project Description

Epigenetics: modifications to DNA and histones that change the expression
of genes but do not alter the code of the genes, are critical to the
development, specialisation and maintenance of different tissues, but how
they operate differentially on a tissue-to-tissue basis is poorly understood.

The two supervisors work with two different epithelial tissue types: breast and
bladder, providing tractable experimental systems for comparative study.

The principal supervisor previously developed VULCAN, a machine learning
method, to elucidate transcriptional co-regulators. In this project, we plan to
adapt this approach to elucidate mechanism of EZH2 regulation in breast
epithelial tissues. Once established, we will apply the same methods to the
alternatively specialised epithelial tissues of the bladder, the urothelium.

From this we will undertake a comparative analysis to investigate the
epigenetic origins of tissue-specific differences. We will additionally validate
the differences in the epigenetic regulators we identify experimentally in the
lab. Building on our experience in studying the breast and bladder epithelium, the
results of this work will provide information on the basic biology of these
tissues and new insights that will be applicable to understanding changes that
occur during healthy and dysregulated ageing.

Funding Notes

This a BBSRC White Rose DTP studentship fully funded for four years and covers: (i) a tax-free annual stipend at the standard Research Council rate (£15,009 estimated for 2020 entry), (ii) research costs, and (iii) tuition fees at the UK/EU rate.

References

Entry requirements: Students with, or expecting to gain, at least an upper second class honours degree, or equivalent, are invited to apply. The interdisciplinary nature of this research project means that we welcome applications from students with backgrounds in any biological, chemical, and/or physical science, or students with mathematical backgrounds who are interested in using their skills in addressing biological questions.

How good is research at University of York in Biological Sciences?

FTE Category A staff submitted: 44.37

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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