Plant stress digital twin module for agricultural and biochemical product development

Overview

This offering provides a digital twin module for plant stress response, designed to help agricultural and biotechnology companies identify molecular markers and targets for new product development. The technology addresses a fundamental challenge in plant science: understanding how plants balance growth and defence when exposed to stress conditions such as drought, pathogens, or temperature extremes.

The solution integrates a comprehensive mathematical model of plant stress signalling with high-resolution time-series measurements to infer the most important pathway crosstalks. By simulating stress responses at the molecular level, partners can accelerate the discovery of intervention points for crop protection, stress tolerance, and yield improvement strategies.

Technical specifications

Core technology components:

• Plant Stress Signalling model (PSS): A curated knowledge graph containing over 500 reactions covering stress factors, perception mechanisms, signalling cascades, hormone metabolism, protective processes, and growth regulators in Arabidopsis thaliana
• Multi-format simulation capability: Supports Boolean rules, semi-quantitative models, and ordinary differential equation (ODE) frameworks using established tools such as Tellurium
• Pathway coverage: Encompasses major stress hormones including abscisic acid (ABA), jasmonic acid (JA), salicylic acid (SA), ethylene (ET), and gibberellins (GA), along with calcium signalling and reactive oxygen species (ROS) regulatory hubs
• Validated foundation: Built on systematic literature curation and has been previously used to predict and experimentally confirm crosstalks between ABA, JA, SA, and other pathways

Key applications:

• Identification of molecular markers for breeding stress-tolerant crops
• Target discovery for novel agrochemicals and biostimulants
• Prediction of plant responses to combined stress conditions
• Rational design of crop protection strategies

Technology readiness level

The underlying PSS knowledge graph has been validated through multiple European research projects and has demonstrated predictive capability for pathway crosstalks. The model has been used to identify and experimentally validate crosstalks between ABA, JA, and SA pathways, as well as calcium-dependent regulatory hubs of ROS signalling.

Current development focuses on transforming the curated network into parameterized mathematical formalisms and implementing simulation frameworks. Future work will integrate this stress module with complementary modules including metabolic models, gene regulatory networks, and gene expression prediction models toward a complete plant digital twin. The technology is positioned for collaborative refinement and validation with industry partners interested in agricultural product development.