Machine Learning Approaches to decipher the epigenetic code in transcription regulation

• Funding – self-funded/externally sponsored applicants  (PhD Fees can be found here)
• Applications are accepted year round
• Standard Entry dates – January and September
• Applicants are expected to have a degree (equivalent of Honours or Masters) in a relevant discipline.

The cells in your retina that fire as you read this text, the neurones in your brain that help you make sense of its content, the muscle cells that make your heart beat: They all contain the same genetic code, the identical DNA molecules, passed down during countless cell divisions from a single zygote. And yet, all these cells come in strikingly different shapes and with highly specialised functions. These phenotypical differences are manifestations of the activation and silencing of distinct genetic programs. Regulation of these programs happens through the tightly orchestrated binding and dissolution of myriad transcription factors. These binding events are facilitated - or prevented - by epi-genetic changes to the chromatin landscape. Epigenetic mechanisms are, for instance, chemical changes to the DNA molecule that do not change the DNA sequence itself but affect its local accessibility. These epigenetic mechanisms thus allow for the required plasticity during differentiation but also contribute towards maintaining cellular identity. Malfunction of the epigenetic machinery is, therefore, highly associated with many pathologies, including developmental disorders and tumorigenesis. Epigenetic events leave lasting footprints on the genome, which can be measured using high-throughput sequencing tools. As epigenomic patterns differ between cell types, integrate external signals and dynamically change during ageing, understanding their significance for transcription regulation remains challenging. 

This project will investigate and develop new Machine Learning approaches to analyse and interpret state-of-the-art interventional single-cell time-course experiments on engineered ES cell lines, interrogating the epigenetic machinery and its action on gene regulation and expression. The student will join the Computational Epigenomics Group in the School of Life Sciences and will closely collaborate with experimentalists in the division for Molecular Cell and Developmental Biology. An appetite to learn across disciplines is strongly required for this project. 

Upon completing their PhD, the student will have gained new skills encompassing state-of-the-art machine learning methods, epigenetics, chromatin biology, gene regulation, and sequencing data analysis.

The student will benefit from interactions with a diverse and multidisciplinary scientific community, and will use state-of-the-art facilities. 

Further reading:

1. Blümli et al (2021). Acute depletion of the ARID1A subunit of SWI/SNF complexes reveals distinct pathways for activation and repression of transcription. https://www.sciencedirect.com/science/article/pii/S2211124721014169?via%3Dihub
2. Hawkins-Hooker et al (2023) Getting personal with epigenetics: towards individual-specific epigenomic imputation with machine learning. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406842/
3. Schweikert et al. (2013). MMDiff: quantitative testing for shape changes in ChIP-Seq data sets.  https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-14-826

Our research community thrives on the diversity of students and staff which helps to make the University of Dundee a UK university of choice for postgraduate research.  We welcome applications from all talented individuals and are committed to widening access to those who have the ability and potential to benefit from higher education.