University of Edinburgh Featured PhD Programmes
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University of Edinburgh Featured PhD Programmes
University of Leeds Featured PhD Programmes

Integrative statistical inference methods for eukaryotic gene regulation with applications to embryonic stem cell differentiation


Project Description

Embryonic stem cells (ESCs) can differentiate into different cell types through intermediary cell states and deeper understanding of the regulatory control underlying these differentiation stages is a very important topic in the study of mammalian development (Yang et al., 2014 & 2019). This regulatory control involves complex mechanisms including binding of regulatory proteins (transcription factors) to genomic regulatory regions (enhancers), which interact and regulate proximal as well as distal genes via chromatin looping. Available technologies, e.g., ATAC-seq, HiChIP, single-cell RNA-seq, can quantify different aspects of this hierarchical regulatory system. The statistical and computational challenge is to integrate these data in a modeling framework with efficient model inference methods to unravel the regulatory connections in ESCs differentiation process. Bayesian sparse factor models (Dai et al. 2017, Iqbal et al. 2012) are a popular choice for such data integration and modeling and have been successfully applied in prokaryotic systems with relatively simpler model of gene regulation. In this project, these methods will be extended to deal with new data on chromatin opening and genomic architecture as well as single-cell RNA-seq data in order to uncover key regulators governing the cell state transitions in early mouse embryonic stem cell differentiation. The project will provide training and skills development opportunities in advanced statistical and machine learning methods and will involve collaborative work with experimental groups as well as opportunities to interact with wider data science community under the umbrella of University of Manchester Data Science Institute

Training/techniques to be provided:
Depending on the background of the candidates, relevant training in biology and/or in informatics will be provided through interactions with supervisor, regular group meetings, and also through courses available in the faculty/University, e.g., Wellcome Trust Informatics training program.

Entry Requirements:
Candidates with background in Maths/Physics/Statistics/Engineering/CS with strong interest in biology, or candidates with biological sciences background with some experience in computing and at least good A-level in Maths and strong motivation to learn more advanced statistical modelling techniques.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

Applications are invited from self-funded students. This project has a Band 1 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

References

Yang, S-H., Andrabi, M., Biss, R., Baker, S.M., Iqbal, M., Sharrocks, A.D. (2019) ZIC3 Controls the Transition from Naive to Primed Pluripotency, Cell Reports 27(11), 3215-3227

Dai, Z., Iqbal, M., Lawrence, N.D. and Rattray, M. (2017) Efficient inference for sparse latent variable models of transcriptional regulation Bioinformatics 33(23), 3776-3783

Iqbal, M., Mast, Y., Amin, R., Hodgson, D. A., The STREAM Consortium, Wohlleben, W., and Burroughs, N. J. (2012). Extracting regulator activity profiles by integration of de novo motifs and expression data: characterizing key regulators of nutrient depletion responses in Streptomyces coelicolor. Nucleic Acids Research, 40(12), 5227–5239. http://doi.org/10.1093/nar/gks205

Yang, S-H., Kalkan, T., Morissroe, C., Marks, H., Stunnenberg, H., Smith, A., and Sharrocks, A.D. (2014) Otx2 and Oct4 drive early enhancer activation during ES cell transition from naïve pluripotency. Cell Reports. 7:1968-81.doi: 10.1016/j.celrep.2014.05.037.

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