Computational biology and gene network inference: study the effects of diurnal asymmetric warming on plant defence and growth

Computational biology and gene network inference: study the effects of diurnal asymmetric warming on plant defence and growth

Eligibility: UK/EU/International graduates with the required entry requirements

Funding details: Bursary plus tuition fees

Duration: Full-time – three and a half years fixed term

Application deadline: 29th May 2020

Interview dates: Will be confirmed to shortlisted candidates

Start date: September 2020

For queries contact Dr Fei He.


Project

Plants undergo large-scale changes in gene expression (or transcriptional reprogramming) in response to pathogen infection. The magnitude and rate of transcriptional reprogramming depends on the time of day that the infection occurs. This project aims at investigating the impact of increased temperature, simulating the diurnal asymmetric warming due to climate change, on pathogen-induced changes in plant gene expression at system and network level.

Whole-transcriptome approaches to genetic analyses generate large datasets that allow us to develop gene regulatory networks (GRNs) to understand biological processes better. This systems biology approach also allows us to generate predictive models of how the GRNs may behave in different circumstances and in different plants. In this PhD project, we will limit our analysis to automatic network reconstruction from time-course mRNA data under a nonlinear dynamic systems framework.

This will be an exciting interdisciplinary project that combines systems biology, mathematical modelling, Bayesian/network inference, and machine learning techniques to provide important insights into the way in which plants respond to climate change. The data generated could identify pathways or gene targets as a starting point to develop strategies to improve crop productivity and resilience to climate change. This project will be carried out jointly in two research centres (Centre for Data Science and Centre for Sport, Exercise and Life Sciences) at Coventry University and in collaboration with the University of Cape Town.

Benefits

Training and Development

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Entry requirements

  • A minimum of a 2:1 first degree in a relevant discipline/subject area with a minimum 60% mark in the project element or equivalent with a minimum 60% overall module average.
    PLUS
  • the potential to engage in innovative research and to complete the PhD within a 3.5 years
  • a minimum of English language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component)
Additionally
  • Successful candidates will have at least a minimum 2:1 first degree in Mathematics/Statistics, Computer Science, Engineering, or a related discipline (and preferably a Master degree).
  • Good programming skills (in Matlab, Rython, R or Julia) and strong in mathematics/statistics and numerical analysis.

How to apply

To find out more about the project please contact Dr Fei Hei.

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Apply to Coventry University