Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations

Dhananjay Bhaskar, Daniel Sumner Magruder, Matheo Morales, Edward De Brouwer, Aarthi Venkat, Frederik Wenkel, James Noonan, Guy Wolf, Natalia Ivanova, Smita Krishnaswamy
Proceedings of the Second Learning on Graphs Conference, PMLR 231:22:1-22:21, 2024.

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 simulations of dynamical systems, neuronal networks, and gene regulatory networks, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.

Cite this Paper

BibTeX
@InProceedings{pmlr-v231-bhaskar24a, title = {Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations}, author = {Bhaskar, Dhananjay and Magruder, Daniel Sumner and Morales, Matheo and Brouwer, Edward De and Venkat, Aarthi and Wenkel, Frederik and Noonan, James and Wolf, Guy and Ivanova, Natalia and Krishnaswamy, Smita}, booktitle = {Proceedings of the Second Learning on Graphs Conference}, pages = {22:1--22:21}, year = {2024}, editor = {Villar, Soledad and Chamberlain, Benjamin}, volume = {231}, series = {Proceedings of Machine Learning Research}, month = {27--30 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v231/bhaskar24a/bhaskar24a.pdf}, url = {https://proceedings.mlr.press/v231/bhaskar24a.html}, 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 simulations of dynamical systems, neuronal networks, and gene regulatory networks, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.} }
Endnote
%0 Conference Paper %T Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations %A Dhananjay Bhaskar %A Daniel Sumner Magruder %A Matheo Morales %A Edward De Brouwer %A Aarthi Venkat %A Frederik Wenkel %A James Noonan %A Guy Wolf %A Natalia Ivanova %A Smita Krishnaswamy %B Proceedings of the Second Learning on Graphs Conference %C Proceedings of Machine Learning Research %D 2024 %E Soledad Villar %E Benjamin Chamberlain %F pmlr-v231-bhaskar24a %I PMLR %P 22:1--22:21 %U https://proceedings.mlr.press/v231/bhaskar24a.html %V 231 %X 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 simulations of dynamical systems, neuronal networks, and gene regulatory networks, demonstrating its state-of-the-art capability in inferring interaction graphs compared to previous methods.
APA
Bhaskar, D., Magruder, D.S., Morales, M., Brouwer, E.D., Venkat, A., Wenkel, F., Noonan, J., Wolf, G., Ivanova, N. & Krishnaswamy, S.. (2024). Inferring Dynamic Regulatory Interaction Graphs From Time Series Data With Perturbations. Proceedings of the Second Learning on Graphs Conference, in Proceedings of Machine Learning Research 231:22:1-22:21 Available from https://proceedings.mlr.press/v231/bhaskar24a.html.

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