Overview and Evaluation of Recent Methods for Statistical Inference of Gene Regulatory Networks from Time Series Data

  • Marco Grzegorczyk
  • Andrej Aderhold
  • Dirk HusmeierEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1883)


A challenging problem in systems biology is the reconstruction of gene regulatory networks from postgenomic data. A variety of reverse engineering methods from machine learning and computational statistics have been proposed in the literature. However, deciding on the best method to adopt for a particular application or data set might be a confusing task. The present chapter provides a broad overview of state-of-the-art methods with an emphasis on conceptual understanding rather than a deluge of mathematical details, and the pros and cons of the various approaches are discussed. Guidance on practical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.

Key words

Gene regulatory networks Gaussian graphical models Sparse regression Hierarchical Bayesian models Gaussian processes Bayesian networks Chemical model averaging Bio-PEPA Network inference scoring scheme Circadian regulation Arabidopsis thaliana 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Marco Grzegorczyk
    • 1
  • Andrej Aderhold
    • 2
  • Dirk Husmeier
    • 3
    Email author
  1. 1.Johann Bernoulli InstituteUniversity of GroningenGroningenThe Netherlands
  2. 2.Center for Computer ScienceUniversidade Federal do Rio GrandeRio GrandeBrazil
  3. 3.School of Mathematics and StatisticsUniversity of GlasgowGlasgowUK

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