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Updated: Sep 15, 2025

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Inferring Dynamic Regulatory Interaction Graphs from Time Series Data with Perturbations.

Dhananjay Bhaskar1,2, Daniel Sumner Magruder2, Matheo Morales1

  • 1Department of Genetics, Yale School of Medicine.

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|July 18, 2025
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Summary
This summary is machine-generated.

We introduce Regulatory Temporal Interaction Network Inference (RiTINI) to uncover dynamic relationships in complex systems. RiTINI accurately infers time-varying interaction graphs, outperforming traditional methods for systems biology and network science.

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Area of Science:

  • Complex Systems Science
  • Network Science
  • Computational Biology

Background:

  • Complex systems exhibit dynamic interactions crucial for behavior prediction.
  • Accurate inference of these dynamic relationships remains a challenge for traditional methods.

Purpose of the Study:

  • To develop a novel method for inferring time-varying interaction graphs in complex systems.
  • To overcome limitations of traditional causal inference networks that infer static and acyclic graphs.

Main Methods:

  • Regulatory Temporal Interaction Network Inference (RiTINI) combines space-and-time graph attentions with graph neural ordinary differential equations (ODEs).
  • RiTINI utilizes time-lapse signals and node perturbations to capture system dynamics.
  • Employs graph attention for adaptive focus on relevant interactions and graph neural ODEs for continuous-time modeling.

Main Results:

  • RiTINI successfully infers cyclic, directed, and time-varying graphs, offering a more comprehensive representation.
  • Demonstrated state-of-the-art performance in inferring interaction graphs on simulations of dynamical systems, neuronal networks, and gene regulatory networks.

Conclusions:

  • RiTINI provides a powerful and accurate approach for modeling dynamic interactions in complex systems.
  • The method advances the field of network inference, particularly for biological and dynamical systems.