Phd - BBSRC MIBTP - Evolution and control of endosymbiotic power plants in plant cells

Mitochondria and chloroplasts were originally independent organisms, which were engulfed and harnessed by an ancestral cell in the distant evolutionary past. These organelles retain their own genomes, and continue to replicate, degrade, mutate, and recombine in modern-day plant cells. These rich dynamics make these cellular power plants a fascinating “evolutionary system within cells” – and one that is of fundamental importance in maintaining the energy supplies that plants need to survive and crops need to grow.
Despite the fact that plant organelles feed the world, through photosynthesis and respiration, much remains to be understood about how these populations evolve within cells. This project will use a cutting-edge and highly transferrable set of tools from maths, machine learning, and biology to make progress describing these rich, vital systems.
Throughout evolutionary history, mitochondria and chloroplasts have lost many of their genes to the host cell nucleus. Why they retain the genes they do is currently poorly understood, and of vital importance in understanding how complex life has evolved, and how plants produce energy and food. How the cell maintains the these populations of organellar DNA – power station blueprints – is also of profound importance in understanding crop diseases and stress responses.
Recent work from our group has combined evolutionary modelling with tools from data science to provide answers to some of these questions in mitochondria – identifying the features that dictate whether genes will be retained in mtDNA through evolution [1, 2], and explaining how mtDNA populations avoid the buildup of mutations over time [3]. The parallels to these questions, and many others, in chloroplasts remain unanswered. Progress here will both help elucidate “universal” rules underlying organelle evolution over the billions of years of eukaryotic history, and to understand how plants (and humans) can control their vital energetic organelles. The project will harness the power of interdisciplinary approaches in bioscience both to unpick evolutionary history and help learn how we can optimise the control of energy supply in plants, to design more efficient and robust crops.
The student will learn how to construct stochastic models in biology – that is, quantitative models that describe not only the average behaviour of a system but also its variability. These approaches are of immense importance in the rich, noisy worlds of modern biology and complex systems. They will use biological “big data” – including large-scale genomic datasets – to explore the rich behaviour of organelles in plant cells, and harness tools from data science to explore hypotheses and rigorously compare models on organelle evolution and behaviour. Candidates will also explore experimental approaches to refine and validate hypotheses about organelle evolution, control, and cellular influence in plants.
This project would suit candidates interested in the intersection between “blue skies” evolutionary questions and questions of translatable importance in crop plants. Some mathematical and/or statistical background would be an advantage, and an interest in developing mathematical and data science skills is essential.

Funding Notes

This studentship is competition funded by the BBSRC MIBTP scheme: http://www.birmingham.ac.uk/research/activity/mibtp/index.aspx
Deadline: January 8, 2017
Number of Studentships available: 30
Stipend: RCUK standard rate (plus travel allowance in Year 1 and a laptop).
The Midlands Integrative Biosciences Training Partnership (MIBTP) is a BBSRC-funded doctoral training partnership between the universities of Warwick, Birmingham and Leicester. It delivers innovative, world-class research training across the Life Sciences to boost the growing Bioeconomy across the UK.
To check your eligibility to apply for this project please visit: http://www2.warwick.ac.uk/fac/cross_fac/mibtp/pgstudy/phd_opportunities/application/

References

1. Johnston & Williams, Cell Systems 2 101 (2016)
2. Johnston & Williams, The Scientist 30 22 (2016)
3. Johnston et al., eLife 4 e077464 (2015)