Mathematical approaches in inferring gene regulatory networks mediating the transition between early and late stages of plants' response to drought

Start date: October 2020
Duration: 3 years (full time)
Location: Colchester Campus
Based in: Department of Mathematical Sciences (in collaboration with the School of Life Sciences)

Water limitation in agriculture is increasing due to urbanisation, industrialisation, depletion of aquifers and climate change.
Reduced water availability leads to drought stress, a major constraint on the productivity of crop plants. Understanding the mechanisms of drought response is essential for the improvement of plant performance.

In 2016, the co-supervisors of the project, Dr Ulrike Bechtold and Prof. Phil Mullineaux of the School of Life Sciences, published high-resolution transcriptomics data sets, coupled with detailed physiological and metabolic analyses in Arabidopsis plants that were subjected to a slow transition from well-watered to drought conditions. 1815 drought-responsive differentially expressed genes (DEGs) responded to the transition between early and late stages of drought.

The co-supervisors used Bayesian network modelling of DEGs coding for transcription factors to construct gene regulatory networks (GRNs). This led to the identification of a novel drought responsive signalling network (Bechtold et al. (2016), Plant Cell, 28, 345).
This complex dataset is a comprehensive resource that remains mathematically under-exploited.

The project
In this project, to be supervised primarily by Dr Chris Antonopoulos of the Department of Mathematical Sciences, a leading expert in network inference (see for example Bianco-Martinez et al. (2016), Chaos, 26 (4), 043102), you will develop new mathematical approaches that will find better ways to infer GRNs and model their structure.

The project is expected to lead to improved accuracy of inferring water deficit-associated GRNs that will improve the success rate of identifying novel drought-responsive genes, which will be validated experimentally in Arabidopsis and crop plant species.

Criteria
We are looking for candidates with a BSc in Maths or related subjects, and an MSc in Biology or related subjects.

How to apply
You can apply for this postgraduate research opportunity online (https://www1.essex.ac.uk/pgapply/login.aspx).

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.

Find out more about this studentship and information on how to apply on our website (https://www.essex.ac.uk/postgraduate-research-degrees/opportunities/mathematical-approaches-in-inferring-gene-regulatory-networks).