Understanding regulation of gene expression during development via an integrated computational analysis of ‘omics data

   Faculty of Biology, Medicine and Health

  , ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Embryonic development requires exquisite control of the expression of large numbers of genes, integrating components from the genome, transcriptome and proteome. For one such model organism, Drosophila melanogaster (fruit fly), the developmental transcriptome and proteome have been quantitatively mapped. Additionally, we have novel RNAi datasets linked to Notch signaling, a major player in embryonic development, as well as several microRNA expression datasets. We aim to conduct a series of integrative bioinformatics studies to address a variety of questions in gene expression during development, so examine how all these components come together. For example, microRNAs are non-coding RNAs that are instrumental in the regulation of the expression of many key developmental target genes, influencing gene expression at the post-transcriptional level. By integrating mRNA and microRNA expression levels data, via microRNA target prediction tools, we can test how the proteome will be affected – do protein levels go up or down at given developmental time points? We will use the quantitative proteomics data we have helped generate, with over 6000 proteins in 2-hour timepoints across 24 hours, to evaluate the predictions, seeking to understand how transcriptome and proteome regulation are controlled via microRNAs on a genome-wide basis – supported by direct evidence at the protein level. Similarly, we have begun to integrate our RNAi screening data to generate clusters of genes which share common functional properties, and can integrate this with the quantitative proteomics and protein interaction network data to understand the rules which describe how Notch signaling via two distinct routes is able to change expression of different gene targets. This computational data analysis project has the overall aim of understanding the interplay between the transcriptome and proteome to define the detail of gene regulation during metazoan development at the proteome level.

Training/techniques to be provided:

Computational biology, Quantitative proteome informatics, microRNA bioinformatics, R/python coding skills, knowledge of embryogenesis and development.

Entry Requirements

Candidates are expected to hold (or be about to obtain) a minimum upper second class honours degree (or equivalent) in a related area / subject, ideally in a bioscience or computational science. Candidates with experience in bioinformatics, computer science, software engineering or another related discipline are also encouraged to apply.

How To Apply

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/). Informal enquiries may be made directly to the primary supervisor. On the online application form select the appropriate subject title.

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.

Equality, Diversity and Inclusion

Equality, diversity and inclusion is fundamental to the success of The University of Manchester, and is at the heart of all of our activities. The full Equality, diversity and inclusion statement can be found on the website https://www.bmh.manchester.ac.uk/study/research/apply/equality-diversity-inclusion/”

Biological Sciences (4) Chemistry (6) Mathematics (25)

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).


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Shimizu H, Woodcock SA, Wilkin MB, Trubenová B, Monk NA, Baron M. Compensatory flux changes within an endocytic trafficking network maintain thermal robustness of Notch signaling. Cell. 2014 May 22;157(5):1160-74. doi: 10.1016/j.cell.2014.03.050. PubMed PMID: 24855951; PubMed Central PMCID: PMC4032575.

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