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Related Concept Videos

Protein Networks02:26

Protein Networks

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.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
07:57

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Reconciling differential gene expression data with molecular interaction networks.

Christopher L Poirel1, Ahsanur Rahman, Richard R Rodrigues

  • 1Department of Computer Science, Virginia Tech, Blacksburg, VA, USA.

Bioinformatics (Oxford, England)
|January 15, 2013
PubMed
Summary

This study introduces algorithms to reconcile gene expression data with molecular networks, improving disease-specific pathway identification. These methods uncover novel disease-related functions missed by traditional expression analysis alone.

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Current methods for cell response network computation often focus on small subnetworks, potentially missing broader pathway perturbations in disease.
  • Interpreting small subnetworks is easier, but may not capture the full spectrum of affected pathways.

Purpose of the Study:

  • To develop and evaluate algorithms that reconcile case-control DNA microarray data with molecular interaction networks.
  • To improve the identification of disease-specific gene expression changes and biological pathways.

Main Methods:

  • Algorithms modify per-gene differential expression P-values to ensure connected genes exhibit similar expression changes.
  • Four reconciliation algorithms were evaluated based on specificity, network coherence, and functional enrichment.
  • Evaluation was performed on gene expression data from 54 diverse human diseases.

Main Results:

  • The proposed algorithms generate gene rankings specific to the studied condition.
  • Highly ranked genes formed coherent network structures and were functionally enriched with relevant pathways.
  • Reconciled gene rankings identified novel disease-related functions not detected by analyzing expression data alone.

Conclusions:

  • Reconciling gene expression data with molecular networks offers a more comprehensive approach to understanding disease mechanisms.
  • The developed algorithms enhance the discovery of biologically relevant functions and pathways in various diseases.