PhD Research Project - How do plants roll dice? Genetic features underlying variability in plant de

Plants feed the world, but a fundamental tension exists between the past evolutionary priorities of plants and our current priorities for using them in agricultural settings. Throughout biological history, plants have evolved the ability to “hedge their bets” by having seeds germinate at different times. This makes a given generation of seeds more robust to unpredictable environments. For example, if plant A's seeds spread germination through spring and plant B's seeds all germinate in April, a frost in May will kill all plant B's progeny while some from plant A survive.

In agriculture, we have the ability to control environments, lessening the impact of fluctuations like this. Instead of being useful, germination variability poses a challenge to agriculture, limiting crop yields, allowing pest invasion, and making harvesting difficult. A priority for food security is to decrease this variability in germination.

To this end, we have recently revealed molecular circuitry that generates noise in hormone levels in plant seeds, acting as a “random number generator” helping to generate variability in plant germination. However, the mechanisms underlying this circuitry must be explored further in order to gain useful insight into how we may reduce variability in the field.

An exciting new resource for this investigation is the “1001 Genomes” project, consisting of genome sequences for a vast variety of different accessions of Arabidopsis thaliana, a model plant that serves as a useful scientific tool with which to explore plant behaviour. The 1001 Genomes project allows us to use evolution as a large set of experiments, exploring the diversity that has naturally arisen in Arabidopsis and revealing its effects on the behaviour of plants.

This project will use “big data” approaches in conjunction with new experiments to shed new light on the genetic features underlying germination variability. The researcher will acquire skills in data science and bioinformatics in addition to experimental biology and plant culture, with potential extension in experimental, theoretical, or both directions. We will explore translation of the insights we gain from the model plant Arabidopsis to crop plants and collaborate with experts in Birmingham and internationally in exploring these outstanding genetic questions.

Techniques that will be undertaken during the project:

Bioinformatics, machine learning / data science, plant growth and culture, germination assays