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Detection of activity centers in cellular pathways using transcript profiling.

Joel Pradines1, Laura Rudolph-Owen, John Hunter

  • 1Department of Computational Sciences, Millennium Pharmaceuticals, Inc, Cambridge, Massachusetts 021398, USA. joel.pradines@mpi.com

Journal of Biopharmaceutical Statistics
|October 8, 2004
PubMed
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This summary is machine-generated.

This study introduces a novel computational method to identify biological pathway components from transcript profiling data. The approach uses graph theory to pinpoint "activity centers" representing perturbed pathways, aiding in biological discovery.

Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Transcript profiling (TP) experiments generate large datasets of gene expression.
  • Identifying regulated biological pathways from TP data requires robust analytical methods.
  • Existing methods may not fully leverage network information for pathway analysis.

Purpose of the Study:

  • To develop a new computational method for identifying regulated pathway components in transcript profiling experiments.
  • To evaluate transcriptional activity within the context of known biological pathways.
  • To define and identify
  • activity centers

Main Methods:

  • Constructing a protein functional relationship graph by integrating public databases and literature.

Related Experiment Videos

  • Defining pathway neighborhoods using graph distance.
  • Identifying perturbed pathways as subgraphs induced by genes with high transcriptional activity density in their neighborhoods (activity centers).

    • Main Results:

    • Demonstrated predictive power using TP53 overexpression in NCI-H125 cells, with detected activity centers aligning with known TP53 effects.
    • Applied the method to a serum starvation experiment in HEY cells, predicting MYC transcription factor activity.
    • Validated findings through independent experimental results.

    Conclusions:

    • The activity center approach effectively identifies regulated pathway components from transcript profiling data.
    • This method provides a powerful tool for analyzing gene expression data and understanding pathway perturbations.
    • The approach has potential applications beyond pairwise experiment comparisons.