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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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Gene Expression Analysis Through Network Biology: Bioinformatics Approaches.

Kanthida Kusonmano1

  • 1Bioinformatics and Systems Biology Program, School of Bioresources and Technology, King Mongkut's University of Technology Thonburi, Bangkhuntien, Bangkok, Thailand. kanthida.kus@kmutt.ac.th.

Advances in Biochemical Engineering/Biotechnology
|November 11, 2016
PubMed
Summary

Bioinformatics network analysis integrates gene expression data with interactome data for a systems-level understanding of biological processes. This approach complements traditional gene expression analysis by revealing functional links between biomolecules.

Keywords:
Biological network analysisData integrationGene expression analysisInteractomeNetwork biology

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Network Biology

Background:

  • High-throughput technologies generate vast biological data, including gene expression data, crucial for studying cellular systems at the transcriptional level.
  • Traditional gene expression analysis identifies differentially expressed genes but often overlooks the interactive nature of biomolecules.
  • Understanding biological systems requires considering the network of interactions between biomolecules.

Purpose of the Study:

  • To describe bioinformatics approaches for analyzing gene expression data at the network level.
  • To explain basic concepts of network biology.
  • To detail methods for integrating gene expression data with interactome data.

Main Methods:

  • Description of bioinformatics approaches for network-level analysis of gene expression data.
  • Explanation of network biology concepts.
  • Methods for integrating gene expression data with interactome data.

Main Results:

  • Network analysis provides complementary insights to traditional gene expression analysis.
  • Integration of expression and interactome data reveals functional links between biomolecules.
  • Example studies demonstrate the application of these bioinformatics approaches.

Conclusions:

  • Network-based bioinformatics approaches enhance the understanding of biological systems.
  • Integrating gene expression and interactome data offers a more comprehensive view of cellular processes.
  • This chapter provides a foundation for analyzing biological data in a network context.