Shuting Ding’s research explores how living systems adapt to a rapidly changing climate, focusing on the regulatory machinery that enables plants to survive extreme environments. Unlike mobile organisms, plants must endure environmental stress through complex internal networks that decide which genes to turn on or off. Understanding how these regulatory networks respond under future climate scenarios is essential for protecting global food security. However, moving from observing these networks to predicting their behavior under such conditions remains a fundamental challenge.
Her work combines large-scale single-cell data with AI to build computational models that reveal how plant regulatory systems reorganize under environmental stress. She aims to develop graph-based machine learning models that treat plants as a networked system, learning how its regulatory components interact and shift across species, genotypes, and stress conditions. By integrating AI with single-cell data spanning multiple species and stress conditions, her ultimate goal is to create frameworks that predict how plants respond to new or extreme climate conditions, an essential step toward forecasting resilience and engineering crops better suited for the future.