Imperial College London Featured PhD Programmes
Imperial College London Featured PhD Programmes
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Norwich Research Park Featured PhD Programmes
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Understanding regulation of gene expression during development via an integrated computational analysis of ‘omics data


Project Description

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.

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

Talavera D, Kershaw CJ, Costello JL, Castelli LM, Rowe W, Sims PFG, Ashe MP, Grant CM, Pavitt GD, Hubbard SJ. Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions. Sci Rep. 2018 May 21;8(1):7949. doi: 10.1038/s41598-018-26170-5. PubMed PMID: 29785040
Jarnuczak AF, Albornoz MG, Eyers CE, Grant CM, Hubbard SJ. A quantitative and temporal map of proteostasis during heat shock in Saccharomyces cerevisiae. Mol Omics. 2018 Feb 1;14(1):37-52. doi: 10.1039/c7mo00050b. Epub 2018 Jan 16. PubMed PMID: 29570196.
Lawless C, Holman SW, Brownridge P, Lanthaler K, Harman VM, Watkins R, Hammond DE, Miller RL, Sims PF, Grant CM, Eyers CE, Beynon RJ, Hubbard SJ. Direct and Absolute Quantification of over 1800 Yeast Proteins via Selected Reaction
Monitoring. Mol Cell Proteomics. 2016 Apr;15(4):1309-22. doi:10.1074/mcp.M115.054288. Epub 2016 Jan 10. PubMed PMID: 26750110; PubMed Central PMCID: PMC4824857.
Ninova M, Ronshaugen M, Griffiths-Jones S. MicroRNA evolution, expression, and function during short germband development in Tribolium castaneum. Genome Res.2016 Jan;26(1):85-96. doi: 10.1101/gr.193367.115. Epub 2015 Oct 30. PubMed PMID: 26518483; PubMed Central PMCID: PMC4691753
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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