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(BBSRC DTP) Phenotype prediction of gene-dosage alterations through quantitative modelling

Faculty of Biology, Medicine and Health

About the Project

Saccharomyces cerevisiae is one of the most studied model organisms because of a series of interesting features: 1) it is unicellular; 2) the yeast genome only contains 6,000 genes; 3) there is no alternative splicing; and, 4) it is easy to grow in the lab. This has allowed its wide use in biotechnology and synthetic biology, with industrial applications ranging from biofuels to food production.

Despite its apparent simplicity, yeast is a complex organism: it is constantly sensing the environment in order to decide if it can grow or needs to activate a defence response. This implies a complicated coordination of gene expression at different levels: transcription, translation and protein degradation. Single-gene knock out experiments showed that a fifth of genes are essential for yeast survival, while double-gene knock out experiments demonstrated that concurrent deletion of non-essential genes may be lethal as well. We have shown in the past that essentiality and synthetic lethality are related to participation in protein complexes and metabolic pathways, which are the reason of gene expression coordination. Now, we are interested in the systemic consequences in gene expression coordination of gene deletions, and to better understand the system bottlenecks leading to synthetic lethality. Therefore, we propose the quantitative modelling of the whole yeast gene expression system, and the analysis of perturbations caused by gene deletions.

The project will enhance our understanding of the complexity of gene expression coordination, and open the door to cutting edge biotechnological innovations. Moreover, we are interested in scaling up the model to multicellular organisms, which could help us to understand development programmes and origin of diseases. The student will use a variety of computational approaches including data mining, network analyses, modelling optimisation and single-cell transcriptomics. The project builds on the existing knowledge and complementary expertise of the different laboratories involved. It represents an ideal opportunity for an individual with a keen interest in mathematical and statistical approaches applied to deciphering Biology.

Entry Requirements:
Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

UK applicants interested in this project should make direct contact with the Principal Supervisor to arrange to discuss the project further as soon as possible. International applicants (including EU nationals) must ensure they meet the academic eligibility criteria (including English Language) as outlined before contacting potential supervisors to express an interest in their project. Eligibility can be checked via the University Country Specific information page (
If your country is not listed you must contact the Doctoral Academy Admissions Team providing a detailed CV (to include academic qualifications – stating degree classification(s) and dates awarded) and relevant transcripts.

Following the review of your qualifications and with support from potential supervisor(s), you will be informed whether you can submit a formal online application.

To be considered for this project you MUST submit a formal online application form - full details on how to apply can be found on the BBSRC DTP website

Funding Notes

Funding will cover UK tuition fees/stipend only. The University of Manchester aims to support the most outstanding applicants from outside the UK. We are able to offer a limited number of scholarships that will enable full studentships to be awarded to international applicants. These full studentships will only be awarded to exceptional quality candidates, due to the competitive nature of this scheme.

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 View Website


• Costello JL, Kershaw CJ, Castelli LM, Talavera D, Rowe W, Sims PFG, Ashe MP, Grant CM, Hubbard SJ, Pavitt GD. (2017) Dynamic changes in eIF4F-mRNA interactions revealed by global analyses of environmental stress responses. Genome Biol 18(1):201.
• Kershaw CJ, Costello JL, Talavera D, Rowe W, Castelli LM, Sims PFG, Grant CM, Ashe MP, Hubbard SJ, Pavitt GD. (2015) Integrated multi-omics analyses reveals pleiotropic nature of control of gene expression by Puf3. Sci Rep 5: 15518.
• Talavera D, Robertson DL, Lovell SC. (2013) The role of gene duplications and protein interactions in mediating essentiality. PLoS ONE 8(4): e62866.
• Isik Z , Ersahin T, Atalay V, Aykanat C, Cetin-Atalay R. (2012) A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data. Mol Biosyst 8(12):3224-31.

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