Photosynthetic eukaryotes, such as land plants and green algae, interact with complex communities of heterotrophic microorganisms based on the mutual exchange of metabolic currencies and infochemicals. For microalgae, these communities – collectively termed the ‘phycosphere microbiota’ – have been well studied in aquatic ecosystems, where they play important roles in nutrient and energy fluxes. Exploring the role of terrestrial microalgae as hosts of complex microbial communities could improve our understanding of soil microbial ecology, host-microbiota interactions, and of carbon and nutrient cycles in terrestrial ecosystems.
Studying how natural communities of diverse organisms change over time is one of the central goals of ecological theory. The observation that even simple communities can display chaotic behaviour has led to the recognition that predicting the fate of an ecological system from its current state is an important but challenging problem.
This project will employ a reductionist experimental system based on microscopic algae and synthetic bacterial communities. The student will receive multi-disciplinary training and, in addition to skills in molecular biology and ecology, learn how to apply machine learning approaches, such as deep neural networks to complex biological data. This will provide the tools necessary to assess how changing environmental conditions affect our ability to predict ecosystem stability and future behaviour. If successful, the knowledge generated in this project could assist in efforts in ecosystem restoration and have diverse applications in biotechnology.
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