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Related Experiment Videos

Reverse-engineering gene-regulatory networks using evolutionary algorithms and grid computing.

Martin Swain1, Thomas Hunniford, Werner Dubitzky

  • 1School of Biomedical Sciences, University of Ulster, Coleraine, Northern Ireland. mt.swain@ulster.ac.uk

Journal of Clinical Monitoring and Computing
|December 6, 2005
PubMed
Summary
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Computational models built with evolutionary algorithms help understand complex gene-regulatory networks. Grid computing provides the necessary power for this analysis, revealing network dynamics.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Gene expression is regulated by complex interactions involving transcription factors, mRNA, and proteins.
  • Gene-regulatory networks (GRNs) are intricate and not fully understood.
  • Computational modeling offers a pathway to understanding GRN function and dynamics.

Purpose of the Study:

  • To develop computational models of gene-regulatory networks using evolutionary algorithms.
  • To investigate the application of grid computing infrastructure for intensive modeling tasks.
  • To assess the suitability of Condor and JavaSpaces for GRN modeling.

Main Methods:

  • Utilized evolutionary algorithms to construct GRN models from microarray data.
  • Implemented modeling approach within a grid computing infrastructure designed for data mining.

Related Experiment Videos

  • Leveraged distributed computing to manage computationally intensive algorithmic processes.

    • Main Results:

    • Demonstrated the feasibility of building GRN models using distributed and grid computing.
    • Evaluated the effectiveness of Condor and JavaSpaces for supporting the computational demands of evolutionary algorithms in GRN modeling.
    • Showcased how grid computing enhances the capacity for complex biological network analysis.

    Conclusions:

    • Modeling GRNs with evolutionary algorithms necessitates substantial computational resources.
    • A robust modeling formalism is crucial for elucidating the underlying dynamics of gene regulation.
    • Grid computing is a viable solution for addressing the computational challenges in GRN modeling.