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Ridge estimation of network models from time-course omics data.

Viktorian Miok1,2, Saskia M Wilting1, Wessel N van Wieringen2,3

  • 1Department of Pathology, VU University Medical Center, MB, Amsterdam, The Netherlands.

Biometrical Journal. Biometrische Zeitschrift
|August 24, 2018
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Summary

This study presents a new method to reconstruct cellular regulatory network dynamics from time-course omics data using vector-autoregressive models. The approach aids medical researchers in understanding molecular interactions and signaling pathways.

Keywords:
cervical cancerconstrained estimationmaximum likelihoodmutual informationpath analysispenalized estimationtime series analysisvector autoregressive process

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Network Medicine

Background:

  • Time-course omics experiments provide dynamic snapshots of cellular processes.
  • Understanding cellular regulatory networks is crucial for deciphering disease mechanisms.
  • Existing methods may not fully capture the dynamic interactions within these networks.

Purpose of the Study:

  • To develop and present a methodology for reconstructing dynamic cellular regulatory networks from time-course omics data.
  • To enable downstream exploitation of inferred networks for biological insights.
  • To provide tools applicable to medical research.

Main Methods:

  • Utilizing vector-autoregressive (VAR) models to describe time-course omics data.
  • Employing ridge penalized likelihood maximization for model estimation, with optimal penalty parameter determination.
  • Incorporating prior knowledge of network topology into estimation procedures.
  • Inferring the network using empirical Bayes probabilistic thresholding on non-sparse ridge estimates.

Main Results:

  • Successful reconstruction of cellular regulatory network dynamics.
  • Assessment of molecular entity influence over time using mutual information, impulse response analysis, and covariance path decomposition.
  • Application of the methodology to p53 signaling pathway data during HPV-induced cellular transformation.

Conclusions:

  • The developed methodology offers a robust framework for dynamic network reconstruction from omics data.
  • The inferred networks provide tangible implications for medical researchers.
  • The ragt2ridges R package implements the described methodology for accessibility.