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Protein Networks02:26

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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Signaling cascades usually lack linearity. Multiple pathways interact and regulate one another, allowing cells to integrate and respond to diverse environmental stimuli.
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Identifying Gene Interaction Networks.

Danica Wiredja1,2,3, Gurkan Bebek4,5,6,7

  • 1Systems Biology and Bioinformatics Graduate Program, Case Western Reserve University School of Medicine, Cleveland, OH, 44116, USA.

Methods in Molecular Biology (Clifton, N.J.)
|October 6, 2017
PubMed
Summary
This summary is machine-generated.

This chapter introduces interaction networks, focusing on publicly available data and utilizing Cytoscape for analysis. Learn to build, visualize, and analyze biological networks for research insights.

Keywords:
CytoscapeEnrichmentGene ontologyInteraction networksNetworkPathwaysProtein–protein interactions

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Science

Background:

  • Interaction networks are crucial for understanding complex biological systems.
  • Publicly available datasets offer valuable resources for network analysis.
  • Cytoscape is a powerful, open-source tool for network visualization and analysis.

Purpose of the Study:

  • To introduce the generation, storage, and sharing of interaction networks.
  • To demonstrate practical applications of network analysis using Cytoscape.
  • To guide users in utilizing public interaction network resources.

Main Methods:

  • Utilizing publicly available interaction network databases.
  • Building and analyzing networks with Cytoscape.
  • Performing topological analysis and gene ontology enrichment.
  • Constructing networks from experimental gene expression data.
  • Implementing advanced network visualization and subnetwork enrichment.

Main Results:

  • Demonstrated basic Cytoscape functionalities for network construction and analysis.
  • Successfully performed gene ontology enrichment on a public network.
  • Constructed a novel network from gene expression data.
  • Applied advanced visual annotations and subnetwork enrichment.

Conclusions:

  • Cytoscape provides a user-friendly platform for diverse network analysis tasks.
  • Methods are applicable to various interaction networks and datasets.
  • Analysis of interaction networks aids in uncovering biological insights.