Gene Regulatory Analysis for Stress in Plants

Objectif

Global food security is increasingly threatened by climate change, population growth, and the rapid spread of plant diseases, which together cause substantial crop losses and economic damage worldwide. Developing crop varieties with improved disease resistance is therefore a major priority for sustainable agriculture. However, breeding for disease resistance remains challenging because the underlying regulatory mechanisms controlling plant immune responses are highly complex.
The GRASP (Gene Regulatory Analysis for Stress in Plants) project aims to accelerate crop improvement by advancing PlantDynNet, an artificial intelligence (AI)-based platform for reconstructing dynamic gene regulatory networks (GRNs) in plants. By integrating computational modelling with experimental validation, the project will generate biologically interpretable insights into plant immune regulation, providing researchers and breeders with improved tools to identify candidate genes associated with disease resistance.
During the ERC Proof of Concept project, PlantDynNet will be further developed into a robust and user-friendly platform, validated in representative crop systems, and assessed for its potential to support crop breeding and agricultural innovation. The project will also evaluate opportunities for future translation and engagement with stakeholders across the plant breeding and agri-food sectors, helping bridge the gap between fundamental plant science and practical agricultural applications.