Modelling seed germination under variable environmental conditions

This PhD project will be jointly supervised by Dr Paula Kover (Department of Biology & Biochemistry) and Dr Christian Yates (Department of Mathematical Sciences).

Seeds ensure plant species’ survival over time and provide food security. The fact that they can remain dormant in the soil for a long time ensures that populations can re-start after being temporarily wiped out. However, if they remain dormant, they will not serve their purpose of starting the next generation. Therefore, the switch that controls when to germinate and when to remain dormant is key to the long term persistence of plant populations. A “stochastic switch” may be favoured as a bet-hedging strategy under natural conditions with variable environmental conditions. However, crop management and harvesting is facilitated by fast and simultaneous germination of crop seeds. Thus, under an agricultural setting a “deterministic switch” would be more desirable.

It is thought that seed dormancy is determined by genetic factors that specify a set of rules used to evaluate environmental cues and decide when it is appropriate to start the germination process. The contribution of genetic and environmental factors have been studied using deterministic models based, for example, on hormonal or temperature thresholds required to break dormancy. At an ecological level, germination is often modelled empirically as a function of environmental factors without considering the dormancy status of the seeds in the soil, which is assumed to vary over time. In practice, it is known that seeds with the same set of rules (i.e. genetically identical) exposed to the same environmental conditions, don’t always germinate at the same time, suggesting the existence of a stochastic element modulating the fate of individual seeds.

This project will develop an individual-based markov chain model of the genetic regulatory system that governs the germination/dormancy mechanism. The model will be able to represent both deterministic and stochastic switches in order to better integrate ecological and physiological controls of seed germination and will be analysed using cutting edge numerical and analytical techniques.

The goal of the project is to increase the predictability of germination models under a number of physiological and environmental variables. Such models would allow the isolation of the factors that modulate the synchronicity of germination. The model will be tested and refined using a genetically variable mapping population of the model species Arabidopsis thaliana under a number of environmental conditions. In addition, the model will be used to determine the environmental conditions under which a stochastic switch would be favoured over a more synchronous and deterministic switch.

The student in this project will receive a multidisciplinary training in the latest techniques in stochastic modelling, analysis and simulation; much needed skills for this interdisciplinary systems biology project. This project has potential applications for plant sciences, weed management, population perseverance in the face of climate change and food security, which the student can choose to pursue.

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree, or the equivalent from an overseas university. A master’s level qualification would also be advantageous. We would welcome both biologists with an interest in learning modelling as well as mathematicians that would like some hands-on data collection.

Informal enquiries should be directed to Dr Paula Kover ([Email Address Removed]) or Dr Christian Yates ([Email Address Removed]).

Formal applications should be made via the University of Bath’s online application form for a PhD in Biology:
https://www.bath.ac.uk/samis/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RDUBB-FP02&code2=0012

More information about applying for a PhD at Bath may be found here:
http://www.bath.ac.uk/guides/how-to-apply-for-doctoral-study/

Anticipated start date: 1 October 2018.