Gene Regulatory Networks

1. Front Matter

   Pages i-xi

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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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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.

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

“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)

• 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

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