Gene Regulatory Networks: Methods and Protocols

✍ Scribed by Guido Sanguinetti, Vân Anh Huynh-Thu

• Publisher: Springer New York,Humana Press
• Year: 2019
• Tongue: English
• Leaves: 433
• Series: Methods in Molecular Biology 1883
• Edition: 1st ed.
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xi
Gene Regulatory Network Inference: An Introductory Survey (Vân Anh Huynh-Thu, Guido Sanguinetti)....Pages 1-23
Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks (Frank Dondelinger, Sach Mukherjee)....Pages 25-48
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
Whole-Transcriptome Causal Network Inference with Genomic and Transcriptomic Data (Lingfei Wang, Tom Michoel)....Pages 95-109
Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks (Alex White, Matthieu Vignes)....Pages 111-142
A Multiattribute Gaussian Graphical Model for Inferring Multiscale Regulatory Networks: An Application in Breast Cancer (Julien Chiquet, Guillem Rigaill, Martina Sundqvist)....Pages 143-160
Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks (Alireza Fotuhi Siahpirani, Deborah Chasman, Sushmita Roy)....Pages 161-194
Unsupervised Gene Network Inference with Decision Trees and Random Forests (Vân Anh Huynh-Thu, Pierre Geurts)....Pages 195-215
Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3 (Vân Anh Huynh-Thu, Guido Sanguinetti)....Pages 217-233
Network Inference from Single-Cell Transcriptomic Data (Helena Todorov, Robrecht Cannoodt, Wouter Saelens, Yvan Saeys)....Pages 235-249
Inferring Gene Regulatory Networks from Multiple Datasets (Christopher A. Penfold, Iulia Gherman, Anastasiya Sybirna, David L. Wild)....Pages 251-282
Unsupervised GRN Ensemble (Pau Bellot, Philippe Salembier, Ngoc C. Pham, Patrick E. Meyer)....Pages 283-302
Learning Differential Module Networks Across Multiple Experimental Conditions (Pau Erola, Eric Bonnet, Tom Michoel)....Pages 303-321
Stability in GRN Inference (Giuseppe Jurman, Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Cesare Furlanello)....Pages 323-346
Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling (Olivia Angelin-Bonnet, Patrick J. Biggs, Matthieu Vignes)....Pages 347-383
Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes (Fabian Fröhlich, Carolin Loos, Jan Hasenauer)....Pages 385-422
Back Matter ....Pages 423-430

✦ Subjects

Chemistry; Biotechnology