Overview
The rhizosphere microbiome acts as an adaptable "external organ" for plants, essential for nutrient acquisition and disease defence. While harnessing these microbes is the key to sustainable agriculture, current bio-fertilizers are often developed via trial-and-error and fail because they ignore the complex interactions that drive community stability.
About this opportunity
To solve this, we must shift from descriptive ecology to a predictive engineering framework. This PhD project employs a “Design-Build-Test-Learn” cycle to deconstruct the “chemical language” of soil microbes and use these rules to rationally design stable, functional synthetic communities (SynComs).
What will you do:
Working with a unique collection of ~40 soil bacterial isolates, you will integrate computational modelling, high-throughput experimentation, and synthetic biology.
· You will mine the genomes of your isolates using state-of-the-art bioinformatics tools to map their metabolic potential and build the project database.
· Using a cutting-edge co-culturing platform, you will generate high-resolution growth curves for pairwise co-cultures and use the data to build a computational predictive framework to describe microbial community dynamics.
· For the most interesting interactions, you will use metabolic modelling and omics profiling to identify the specific diffusible molecules driving these interactions.
Training and Impact:
This is a CASE studentship in partnership with MORF-Bio, a leading industrial biotechnology company in York. You will receive interdisciplinary training in computational biology, High-Performance Computing (HPC), and wet-lab synthetic biology.
By identifying the metabolic rules governing microbe-microbe interactions, you will help create the next generation of bio-fertilizers, reducing reliance on chemical inputs and contributing to global food security.
Applicants are expected to hold (or about to obtain) a minimum upper second-class undergraduate honours degree (or equivalent) in a relevant subject area. Research experience in computational biology, bioinformatics, microbiology, or systems biology is highly desirable.