Project GENIAL

From scientific machine learning to topological data analysis: an innovative framework to improve gene regulatory network dynamic rewiring for plant health

About the project

The goal of this project is to study how plants respond to combined abiotic and biotic stresses exacerbated by harsh climate change. Using tomato (Solanum lycopersicum) as a model species, a novel framework will be developed combining models from systems biology and topological data analysis (TDA). These models will be deployed to understand time-dependent gene regulatory networks (GRNs) derived from the POMOdOROO database, a high-quality database of tomato subjected to 39 pathogens, 6 abiotic stresses and 5 omics types.

Focusing on the dynamic rewiring of GRNs during stress exposure, this interdisciplinary project will 1. develop PIMENTO, a physics-informed neural network used for modelling GRNs, and 2. introduce time-dependent versions of the Mapper and ToMATo algorithms for GRN analyses. This project will produce a comprehensive map of dynamic regulatory mechanisms driving tomato responses to multi-stress conditions. Understanding these dynamic networks is crucial for developing disease-resistant crops and sustainable agricultural practices.