Interventional World Models with Conditional Gene Graphs

Predicting cellular responses to unseen intervention combinations requires modeling how interventions transform cellular states and interact. We introduce the Interventional Graph World Model (IGWM), which represents interventions as reusable state transition operators rather than endpoint labels. IGWM learns a state and intervention conditioned graph editor that selects local gene interactions from a candidate graph. Message passing on the resulting sparse graph defines a conditional velocity field for response prediction. Flow matching enables learning from unpaired endpoint single cell data without longitudinal trajectories. This design supports direct prediction and sequential application of interventions. Across two datasets, IGWM-sparse improves all reported mean metrics over prior baselines for unseen combinations on Norman19 and held-out cells under observed interventions on VCC-H1. IGWM provides a structured framework for modeling context dependent intervention dynamics.