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Gene Regulatory Networks

Methods and Protocols

  • Book
  • © 2019

Overview

Editors:
  1. Guido Sanguinetti

    1. School of Informatics, University of Edinburgh, Edinburgh, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. Vân Anh Huynh-Thu

    1. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Includes cutting-edge methods and protocols
  • Provides step-by-step detail essential for reproducible results
  • Contains key notes and implementation advice from the experts

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

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Table of contents (16 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Gene Regulatory Network Inference: An Introductory Survey

    • Vân Anh Huynh-Thu, Guido Sanguinetti
    Pages 1-23

  3. Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks

    • Frank Dondelinger, Sach Mukherjee
    Pages 25-48

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

    • Marco Grzegorczyk, Andrej Aderhold, Dirk Husmeier
    Pages 49-94

  5. Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data

    • Lingfei Wang, Tom Michoel
    Pages 95-109

  6. Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

    • Alex White, Matthieu Vignes
    Pages 111-142

  7. A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer

    • Julien Chiquet, Guillem Rigaill, Martina Sundqvist
    Pages 143-160

  8. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks

    • Alireza Fotuhi Siahpirani, Deborah Chasman, Sushmita Roy
    Pages 161-194

  9. Unsupervised Gene Network Inference with Decision Trees and Random Forests

    • Vân Anh Huynh-Thu, Pierre Geurts
    Pages 195-215

  10. Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3

    • Vân Anh Huynh-Thu, Guido Sanguinetti
    Pages 217-233

  11. Network Inference from Single-Cell Transcriptomic Data

    • Helena Todorov, Robrecht Cannoodt, Wouter Saelens, Yvan Saeys
    Pages 235-249

  12. Inferring Gene Regulatory Networks from Multiple Datasets

    • Christopher A. Penfold, Iulia Gherman, Anastasiya Sybirna, David L. Wild
    Pages 251-282

  13. Unsupervised GRN Ensemble

    • Pau Bellot, Philippe Salembier, Ngoc C. Pham, Patrick E. Meyer
    Pages 283-302

  14. Learning Differential Module Networks Across Multiple Experimental Conditions

    • Pau Erola, Eric Bonnet, Tom Michoel
    Pages 303-321

  15. Stability in GRN Inference

    • Giuseppe Jurman, Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Cesare Furlanello
    Pages 323-346

  16. Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling

    • Olivia Angelin-Bonnet, Patrick J. Biggs, Matthieu Vignes
    Pages 347-383

  17. Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

    • Fabian Fröhlich, Carolin Loos, Jan Hasenauer
    Pages 385-422

  18. Back Matter

    Pages 423-430
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Keywords

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 addressthe common challenges faced by specialists in this field.


Reviews

“The book covers a variety of computational and mathematical aspects related to the inference of gene regulatory networks. The style of the chapters seamlessly binds the comprehensive overview that recommended the book to junior researchers and the thorough description of topics, highlighting new direction of research, that would appeal to post-graduates and established researchers.” (Irina Ioana Mohorianu, zbMATH 1417.92005, 2019)

Editors and Affiliations

  • School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  • Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

Bibliographic Information

  • Book Title: Gene Regulatory Networks

  • Book Subtitle: Methods and Protocols

  • Editors: Guido Sanguinetti, Vân Anh Huynh-Thu

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-4939-8882-2

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: Springer Science+Business Media, LLC, part of Springer Nature 2019

  • Hardcover ISBN: 978-1-4939-8881-5Published: 14 December 2018

  • eBook ISBN: 978-1-4939-8882-2Published: 13 December 2018

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 430

  • Number of Illustrations: 43 b/w illustrations, 71 illustrations in colour

  • Topics: Biotechnology

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