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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,...
Proteomics01:33

Proteomics

A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term proteomics...

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Identifying differentially regulated subnetworks from phosphoproteomic data.

Martin Klammer1, Klaus Godl, Andreas Tebbe

  • 1KINAXO Biotechnologies GmbH, Am Klopferspitz 19a, 82152 Martinsried, Germany.

BMC Bioinformatics
|June 30, 2010
PubMed
Summary
This summary is machine-generated.

SubExtractor integrates phosphoproteomic data with protein networks to identify regulated subnetworks. This algorithm aids in discovering drug mechanisms of action and analyzing gene expression data.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput methods detect regulations at transcription, translation, and post-translation levels.
  • Integrating omics data with protein networks aids in identifying significantly regulated subnetworks.
  • Phosphoproteomic data analysis can reveal signal transduction pathways and drug mechanisms of action.

Purpose of the Study:

  • To introduce SubExtractor, an algorithm for identifying differentially regulated subnetworks.
  • To integrate phosphoproteomic data with protein network information for enhanced biological insights.
  • To facilitate the identification of drug modes of action from experimental data.

Main Methods:

  • SubExtractor algorithm combines phosphoproteomic data with STRING protein network information.
  • Utilizes a Bayesian probabilistic model integrated with a genetic algorithm.
  • Employs rigorous significance testing to ensure reliable results.

Main Results:

  • SubExtractor successfully identifies differentially regulated subnetworks and individual proteins.
  • The method was validated using artificial data.
  • Applied to a phosphoproteomics study of sorafenib's mode of action, demonstrating its practical utility.

Conclusions:

  • SubExtractor reliably identifies differentially regulated subnetworks by integrating phosphoproteomic data and protein networks.
  • The algorithm is effective in analyzing complex biological data.
  • The method is adaptable for gene or protein expression data analysis.