Digital twin model for predicting plant virus transport and targeted interventions

Overview

Plant viruses cause significant agricultural damage by spreading from an initial infection site throughout the entire plant, then transmitting to neighbouring plants via animal vectors. This project develops a digital twin of the host plant's transport network to predict how viruses exploit these pathways and to identify targeted interventions that suppress viral spread. By modelling both local cell-to-cell movement and long-distance vascular transport, the platform aims to reduce viral load in crops and limit the scale of outbreaks.

Technical specifications

Core approach:

• Hybrid digital twin combining a physical model of flow through the plant's stem with a phenomenological model where mechanistic detail is incomplete
• Incorporates imaging data from fluorescently labelled viruses to track infection spread in real time
• Integrates single-cell transcriptomic data from virally infected tissue to capture gene-expression dynamics at cellular resolution
• Designed to describe existing experimental observations and predict outcomes of interventions such as genetic modifications

Key applications:

• Identifying genes and tissue-level processes critical for viral cell-to-cell transport
• Testing strategies for preventing transfer of viruses into the vasculature
• Evaluating the effectiveness of genetic or chemical interventions before field deployment
• Supporting breeding and crop-protection programmes targeting virus resistance

Technology readiness level

The platform is at an early-to-mid stage of development. The underlying methodology of combining digital twins with single-cell omics has been validated in human disease research, demonstrating its effectiveness for identifying influential genes. Current work focuses on constructing the plant-specific model using existing literature on virus spread alongside new imaging and single-cell transcriptomics experiments. Further validation will involve testing the model's predictive capacity against experimental interventions, with the goal of enabling researchers to screen suppression strategies in silico before committing to costly laboratory or field trials.