Inferring dynamic regulatory interaction graphs from time series data with perturbations

Understand

Complex systems are characterized by intricate interactions between entities that evolve dynamically over time.

• Accurate inference of these dynamic relationships is crucial for understanding and predicting system behavior.
• In this paper, we propose Regulatory Temporal Interaction Network Inference (RiTINI) for inferring time-varying interaction graphs in complex systems using a novel combination of space-and-time graph attentions and graph neural ordinary differential equations (ODEs).
• RiTINI leverages time-lapse signals on a graph prior, as well as perturbations of signals at various nodes in order to effectively capture the dynamics of the underlying system.