Gene Regulatory Networks

Methods and Protocols

  • Guido Sanguinetti
  • Vân Anh Huynh-Thu

Part of the Methods in Molecular Biology book series (MIMB, volume 1883)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Vân Anh Huynh-Thu, Guido Sanguinetti
    Pages 1-23
  3. Alireza Fotuhi Siahpirani, Deborah Chasman, Sushmita Roy
    Pages 161-194
  4. Vân Anh Huynh-Thu, Pierre Geurts
    Pages 195-215
  5. Vân Anh Huynh-Thu, Guido Sanguinetti
    Pages 217-233
  6. Helena Todorov, Robrecht Cannoodt, Wouter Saelens, Yvan Saeys
    Pages 235-249
  7. Christopher A. Penfold, Iulia Gherman, Anastasiya Sybirna, David L. Wild
    Pages 251-282
  8. Pau Bellot, Philippe Salembier, Ngoc C. Pham, Patrick E. Meyer
    Pages 283-302
  9. Pau Erola, Eric Bonnet, Tom Michoel
    Pages 303-321
  10. Giuseppe Jurman, Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Cesare Furlanello
    Pages 323-346
  11. Olivia Angelin-Bonnet, Patrick J. Biggs, Matthieu Vignes
    Pages 347-383
  12. Fabian Fröhlich, Carolin Loos, Jan Hasenauer
    Pages 385-422
  13. Back Matter
    Pages 423-430

About this book


This volume explores recent techniques for the computational inference of gene regulatory networks (GRNs). The chapters in this book cover topics such as methods to infer GRNs from time-varying data; the extraction of causal information from biological data; GRN inference from multiple heterogeneous data sets; non-parametric and hybrid statistical methods; the joint inference of differential networks; and mechanistic models of gene regulation dynamics. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, descriptions of recently developed methods for GRN inference, applications of these methods on real and/ or simulated biological data, and step-by-step tutorials on the usage of associated software tools.

Cutting-edge and thorough, Gene Regulatory Networks: Methods and Protocols is an essential tool for evaluating the current research needed to further address the common challenges faced by specialists in this field.


Bayesian networks Gaussian processes data simulation time series expression single-cell transcriptomic

Editors and affiliations

  • Guido Sanguinetti
    • 1
  • Vân Anh Huynh-Thu
    • 2
  1. 1.School of InformaticsUniversity of EdinburghEdinburghUK
  2. 2.Department of Electrical Engineering and Computer ScienceUniversity of LiègeLiègeBelgium

Bibliographic information

  • DOI
  • Copyright Information Springer Science+Business Media, LLC, part of Springer Nature 2019
  • Publisher Name Humana Press, New York, NY
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-4939-8881-5
  • Online ISBN 978-1-4939-8882-2
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site