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Network Biology

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Cover of 'Network Biology'

Table of Contents

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    Book Overview
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    Chapter 1 Analysis of protein-protein interactions using high-throughput yeast two-hybrid screens.
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    Chapter 2 Identification of mammalian protein complexes by lentiviral-based affinity purification and mass spectrometry.
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    Chapter 3 Protein networks involved in vesicle fusion, transport, and storage revealed by array-based proteomics.
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    Chapter 4 Identification and relative quantification of native and proteolytically generated protein C-termini from complex proteomes: C-terminome analysis.
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    Chapter 5 Construction of protein interaction networks based on the label-free quantitative proteomics.
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    Chapter 6 Studying binding specificities of peptide recognition modules by high-throughput phage display selections.
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    Chapter 7 Array-based synthetic genetic screens to map bacterial pathways and functional networks in Escherichia coli.
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    Chapter 8 Advanced methods for high-throughput microscopy screening of genetically modified yeast libraries.
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    Chapter 9 Pooled lentiviral shRNA screening for functional genomics in mammalian cells.
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    Chapter 10 Plant DNA sequencing for phylogenetic analyses: from plants to sequences.
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    Chapter 11 Using coevolution to predict protein-protein interactions.
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    Chapter 12 Visualizing gene-set enrichment results using the Cytoscape plug-in enrichment map.
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    Chapter 13 Quality control methodology for high-throughput protein-protein interaction screening.
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    Chapter 14 Filtering and interpreting large-scale experimental protein-protein interaction data.
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    Chapter 15 Classification of Cancer Patients Using Pathway Analysis and Network Clustering
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    Chapter 16 Statistical analysis of dynamic transcriptional regulatory network structure.
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    Chapter 17 Imputing and predicting quantitative genetic interactions in epistatic MAPs.
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    Chapter 18 Displaying chemical information on a biological network using Cytoscape.
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    Chapter 19 Modeling of proteins and their assemblies with the integrative modeling platform.
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    Chapter 20 Predicting node characteristics from molecular networks.
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    Chapter 21 Mathematical modeling of biomolecular network dynamics.
Attention for Chapter 18: Displaying chemical information on a biological network using Cytoscape.
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Chapter title
Displaying chemical information on a biological network using Cytoscape.
Chapter number 18
Book title
Network Biology
Published in
Methods in molecular biology, July 2011
DOI 10.1007/978-1-61779-276-2_18
Pubmed ID
Book ISBNs
978-1-61779-275-5, 978-1-61779-276-2
Authors

Iain M. Wallace, Gary D. Bader, Guri Giaever, Corey Nislow

Editors

Gerard Cagney, Andrew Emili

Abstract

Cytoscape is an open-source software package that is widely used to integrate and visualize diverse data sets in biology. This chapter explains how to use Cytoscape to integrate open-source chemical information with a biological network. By visualizing information about known compound-target interactions in the context of a biological network of interest, one can rapidly identify novel avenues to perturb the system with compounds and, for example, potentially identify therapeutically relevant targets. Herein, two different protocols are explained in detail, with no prior knowledge of Cytoscape assumed, which demonstrate how to incorporate data from the ChEMBL database with either a gene-gene or a protein-protein interaction network. ChEMBL is a very large, open-source repository of compound-target information available from the European Molecular Biology Laboratory.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 14%
Austria 2 7%
Canada 1 4%
Sweden 1 4%
Unknown 20 71%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 32%
Student > Master 4 14%
Student > Ph. D. Student 3 11%
Student > Doctoral Student 2 7%
Other 2 7%
Other 6 21%
Unknown 2 7%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 32%
Agricultural and Biological Sciences 7 25%
Computer Science 4 14%
Medicine and Dentistry 3 11%
Chemistry 2 7%
Other 2 7%
Unknown 1 4%