Plant viruses pose a major challenge to global food production. Their ability to evolve, overcome crop defences, cross species barriers and emerge in more damaging forms threatens agricultural sustainability worldwide. Yet we still do not understand one of the key processes driving viral evolution within infected plants: when and where viral genomes recombine to generate new variants. Recombination occurs when two viruses exchange genetic material, creating novel hybrid viruses with potentially altered host ranges, virulence, or ability to overcome host resistance. Understanding the circumstances that promote recombination could reveal opportunities to limit viral evolution before new pathogenic variants emerge. In this project, you will combine computational and experimental approaches to investigate where and when recombination occurs within infected plants, addressing a fundamental question at the intersection of plant pathology, evolution and food security.
A central puzzle motivates this work. Recombination requires different viral strains to encounter one another within the host. However, infections by distinct strains typically form largely non-overlapping patches within plant tissues, limiting opportunities for interaction. How, then, do recombinant viruses arise? To address this question, you will begin by analysing a collection of fluorescence images of real viral infections that reveal how viruses spread through plants and where different strains come into contact. To that end, you will use both traditional image-analysis methods and modern machine-learning approaches.
The insights gained from this analysis will be used to develop a multiscale model of viral spread within the plant, linking processes occurring at the level of individual cells to patterns observed across tissues and the whole organism. You will then use simulations to work backwards from the experimental observations, uncovering the hidden biological processes that shape viral spread and estimating the parameters that govern infection dynamics. The resulting framework will allow you to predict when and where opportunities for recombination are most likely to occur within a plant.
In the second phase of the project, you will test these model predictions experimentally. Working with existing molecular resources, you will be introduced to plant pathology, molecular biology and microbial techniques, allowing you to connect theoretical predictions with real biological systems. You will develop a novel molecular tool to detect viral genome recombination events using engineered split and mutated fluorescent proteins. This system will provide a direct way to identify and quantify recombination within infected plants and to evaluate the predictions generated by the computational models.
This project offers a rare opportunity to combine quantitative image analysis with machine learning, mathematical modelling, simulation and experimental biology within a single research programme. You will be jointly supervised by Dr Mike Deeks, a cell biologist and plant pathologist, and Dr Wolfram Moebius, a physicist with a research programme centring around evolution. Supported by this interdisciplinary team, you will integrate computational and laboratory approaches to address a fundamental question in virus evolution while developing a highly transferable interdisciplinary skill set. The outcomes will advance our understanding of how new plant viruses emerge and could ultimately help inform strategies to reduce the evolutionary potential of viral pathogens threatening UK and global food security.
