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Related Experiment Videos

Gene networks: how to put the function in genomics.

Paul Brazhnik1, Alberto de la Fuente, Pedro Mendes

  • 1Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA.

Trends in Biotechnology
|November 5, 2002
PubMed
Summary

Gene networks offer a simplified view of gene regulation, aiding in understanding cellular function and genome annotation. This review covers methods for constructing these networks from experimental data.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Gene networks model direct interactions between genes, offering a phenomenological approach to regulation.
  • These networks are useful for interpreting transcription profiling data, including microarray results.
  • Current models often omit the proteins and metabolites involved in cellular interactions.

Purpose of the Study:

  • To review concepts and methods for constructing gene networks from experimental data.
  • To highlight the potential of gene networks for understanding cellular dynamics and design principles.
  • To propose gene networks as a tool for unambiguous functional description and genome annotation.

Main Methods:

  • Review of existing concepts and methodologies for gene network construction.

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  • Emphasis on methods utilizing experimental data, particularly transcription profiling.
  • Discussion of data-driven approaches for inferring gene interactions.

    • Main Results:

    • Gene networks provide a logical framework for describing observed gene expression phenomena.
    • The construction of gene networks from experimental data is feasible and informative.
    • These networks can elucidate regulatory principles and cellular functions.

    Conclusions:

    • Gene networks are valuable for understanding cellular function and can be used for genome annotation.
    • Further development of methods for gene network construction from experimental data is crucial.
    • This approach enhances the interpretation of biological data and the understanding of gene regulation.