Inferring dynamic regulatory interaction graphs from time series data with perturbations

TL;DR

This paper introduces RiTINI, a novel method combining graph attention and neural ODEs to infer dynamic, cyclic, and directed interaction graphs in complex systems from time series data with perturbations.

Contribution

RiTINI is the first approach to accurately infer cyclic, directed, and time-varying graphs using a combination of space-time attention and neural ODEs, surpassing traditional static causal inference methods.

Findings

RiTINI outperforms existing methods on simulated datasets.

It accurately captures cyclic and directed interactions.

Demonstrates effectiveness on real-world data.

Abstract

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. This approach is distinct from traditional causal inference networks, which are limited to inferring acyclic and static graphs. In contrast, RiTINI can infer cyclic, directed, and time-varying graphs, providing a more comprehensive and accurate representation of complex systems. The graph attention mechanism in RiTINI allows the model to adaptively focus on the most relevant interactions in time and space, while the graph neural ODEs enable continuous-time modeling of the system's dynamics. We evaluate RiTINI's performance on various simulated and real-world datasets, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.