Bacterial Molecular Networks

Methods and Protocols

Editors:

ISBN: 978-1-61779-360-8 (Print) 978-1-61779-361-5 (Online)

Table of contents (26 protocols)

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  1. Front Matter

    Pages i-xi

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    Pages 1-11

    Bacterial Molecular Networks: Bridging the Gap Between Functional Genomics and Dynamical Modelling

  3. Experimental and in silico approaches to unravel network components

    1. Front Matter

      Pages 13-13

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      Pages 15-33

      Bacterial Interactomes: From Interactions to Networks

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      Pages 35-55

      From Bacterial to Microbial Ecosystems (Metagenomics)

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      Pages 57-80

      Prokaryote Genome Fluidity: Toward a System Approach of the Mobilome

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      Pages 81-91

      Reticulate Classification of Mosaic Microbial Genomes Using NeAT Website

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      Pages 93-106

      From Metabolic Reactions to Networks and Pathways

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      Pages 107-130

      Predicting Metabolic Pathways by Sub-network Extraction

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      Pages 131-165

      Directed Module Detection in a Large-Scale Expression Compendium

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      Pages 167-177

      Using Phylogenetic Profiles to Predict Functional Relationships

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      Pages 179-195

      Extracting Regulatory Networks of Escherichia coli from RegulonDB

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      Pages 197-216

      Browsing Metabolic and Regulatory Networks with BioCyc

  4. Topological analysis of bacterial networks

    1. Front Matter

      Pages 217-217

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      Pages 219-244

      Algorithms for Systematic Identification of Small Subgraphs

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      Pages 245-262

      The Degree Distribution of Networks: Statistical Model Selection

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      Pages 263-280

      MAVisto: A Tool for Biological Network Motif Analysis

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      Pages 281-295

      Using MCL to Extract Clusters from Networks

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      Pages 297-312

      Protein Complex Prediction with RNSC

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      Pages 313-326

      Network Analysis and Protein Function Prediction with the PRODISTIN Web Site

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      Pages 327-342

      Using the NeAT Toolbox to Compare Networks to Networks, Clusters to Clusters, and Network to Clusters

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      Pages 343-373

      Analyzing Biological Data Using R: Methods for Graphs and Networks

  5. Dynamical Modelling

    1. Front Matter

      Pages 375-375

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      Pages 377-407

      Detecting Structural Invariants in Biological Reaction Networks

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