Network topology-based plant growth model for predicting shoot branching phenotype in Arabidopsis

Overview

This solution offers a network topology-based modelling framework for predicting plant phenotype, with a focus on shoot branching in Arabidopsis as a case study. The approach links multiple biological layers—environment, metabolites, long-distance signalling, and protein and gene regulation—through conserved network modules and plant architecture, rather than relying on detailed reaction kinetics. By emphasising network topology, the model aims to be more robust and easier to measure than traditional kinetic approaches. The framework builds on validated work in garden pea and rice and seeks to extend predictive capability to dynamic plant architecture in Arabidopsis, with potential application to broader crop systems.

Technical specifications

• Topology-based modelling: Uses conserved network modules across pea, rice, and Arabidopsis to represent genetic and physiological control of shoot branching.
• Layer integration: Connects gene expression, hormone levels and signalling, metabolites, and environmental inputs into a unified predictive framework.
• Dynamic 3D functional-structural plant model (FSPM): Combines topological network models with a new FSPM to simulate shoot architecture over time.
• Genotype-to-phenotype prediction: Generates a predictive landscape linking genotype, phenotype, environment, and management practices.
• Validated foundations: Builds on published quantitative models of Arabidopsis development and topology-based models of pea shoot branching that successfully identified missing network components later confirmed experimentally.

Technology readiness level

The underlying modelling concepts have been validated through published work in pea and Arabidopsis, including topology-based models that iteratively guided laboratory experiments and identified experimentally verified network components. The current proposal seeks to adapt phenotyping techniques for Arabidopsis, design a new dynamic 3D FSPM, and validate the integrated model against existing phenotypic and molecular datasets. The project is at an early-to-mid research and development stage, with a planned one-year timeline to deliver a validated predictive model. The approach is designed to be transferable to multiple plant systems beyond Arabidopsis.