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Inferring causality in biological oscillators.

Jonathan Tyler1,2, Daniel Forger1,3, Jae Kyoung Kim4,5

  • 1Department of Mathematics, University of Michigan, Ann Arbor, MI 48109, USA.

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|August 31, 2021
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Summary
This summary is machine-generated.

We developed a new computational method to accurately infer regulatory interactions in oscillating biological systems. This approach, Inferring Oscillatory Networks (ION), overcomes limitations of existing methods for analyzing time-series data.

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Area of Science:

  • Systems biology
  • Computational biology
  • Network inference

Background:

  • Identifying regulatory interactions is crucial for understanding biological systems.
  • Time-series data offers opportunities for computational inference of regulations.
  • Existing methods struggle with oscillatory data, distinguishing synchrony from causality or requiring inefficient simulations.

Purpose of the Study:

  • To develop a novel inference method for oscillatory systems.
  • To merge the advantages of model-based and model-free inference approaches.
  • To provide a broadly applicable and user-friendly tool for analyzing noisy, oscillatory time series.

Main Methods:

  • Developed a general model applicable to molecular, neuronal, and ecological oscillatory systems.
  • Created a computational package named ION (Inferring Oscillatory Networks).
  • Validated the method on known oscillatory networks like the repressilator and pS2 promoter cofactor network.

Main Results:

  • The ION method accurately infers positive and negative regulations in oscillatory networks.
  • ION outperforms popular existing inference methods in accuracy and applicability.
  • The method successfully handles noisy, oscillatory time-series data.

Conclusions:

  • ION provides a robust and versatile tool for uncovering regulatory mechanisms in diverse oscillating systems.
  • The developed computational package (ION) enhances the usability of network inference for biological time-series data.
  • This work facilitates deeper understanding of the dynamics underlying biological oscillations.