Title: SCH35: The impact of gene expression noise on plant fitness under stress conditions linked to climate change.
Funding: A full Home/EU fee waiver or equivalent fee discount for overseas students (£5,103 in 2019-20) (further fee details - international students will need to pay the balance of their fees) plus a doctoral stipend equivalent to the RCUK Minimum Doctoral Stipend (£15,285 in 2020-21).
Application deadline: Tuesday 31 March 2020
Start date: October 2020
Duration: 3 years (full time)
Location: Colchester Campus
This opportunity is now closed. View our open opportunities.
Plants are constantly exposed to a wide range of environmental changes. Stress-related gene expression patterns are characterised by distinct transcriptional mechanisms, but may also enhance gene expression noise.
Intrinsic gene expression noise refers to variation that arises from molecular-level ﬂuctuations, and it has been hypothesised that gene expression noise has undergone signiﬁcant evolutionary drifts.
Genome-scale studies on unicellular organisms such as yeast have shown that dose-sensitive genes and proteins forming multicomponent complexes have low gene expression noise, genes responding to changes in the environment display high noise levels.
High levels of gene expression noise need to be balanced with growth-related gene expression programmes, and emerging data suggest that organisms exploit this noise to fuel phenotypic variation.
We hypothesise that in plants, intrinsic gene expression noise may lead to selective advantage under stress and that the overall stochastic features of noise may provide beneficial diversity and survival under stress responses.
This PhD aims to investigate whether evolution has ﬁne-tuned noise-generating mechanisms and genetic network architectures leading to plant diversity enhancing stress responses and survival.
We will use existing timeseries gene expression datasets to investigate the patterns of noise in Arabidopsis exposed to short term (high-light, pathogen infections) and long-term (drought, senescence) stress conditions.
Furthermore, we will identify groups of genes with high levels of inherent noise under control and stress conditions and validate whether high level of noise in specific genes is essential for survival under stress.
The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,285 in 2020-21), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.
Lead supervisorSchool of Life Sciences, University of Essex
Dr Ulrike Bechtold is a Subject Lead in Plant Molecular Biology with 15 years’ experience working on plant abiotic stress responses. In the past 3 years, she has published 2 papers on gene- and metabolite regulatory networks utilising time series datasets, which form the basis for this project. She is an expert in the molecular analysis of plant stress responses integrating large datasets (genomics, phenomics) and targeted gene manipulation to investigate novel signalling pathways.
Co-supervisorSchool of Life Sciences, University of Essex
Dr Radu Zabet is a Lecturer in Genomics with significant expertise in gene regulation, 3D chromatin organisation and epigenetics. He has extensive expertise in noise in gene expression and in the last 10 years published 5 journal publications and 2 conference papers. He is successfully collaborating already with Dr Harrison on statistical models of 3D chromatin organisation.
Co-supervisorDepartment of Mathematical Sciences, University of Essex
Dr Andrew Harrison is a SL in Mathematical Sciences with 20 years of experience of analysing large biological datasets. His laboratory spent 10 years analysing large-scale gene-expression measurements and was funded by a ~£440K grant from the BBSRC. His education was in Astrophysics and he is now a Data Scientist looking at a variety of data analytical projects including working with Dr Zabet on the analysis of genome-interaction experiments.
The ideal candidate will hold a BSc/MSc in Biology or Bioinformatics (preferably MSc).
You can apply for this postgraduate research opportunity online.
Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.
The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.
When you apply online you will be prompted to fill out several boxes in the form:
If you have any informal queries about this opportunity please email the lead supervisor, Dr Ulrike Bechtold (firstname.lastname@example.org)
You can find the terms and conditions of this studentship here (PDF).