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Discovering regulatory and signalling circuits in molecular interaction networks
Trey Ideker1, Owen Ozier, Benno Schwikowski
1Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA Institute for Systems Biology, Seattle, WA 98103, USA. trey@wi.mit.edu
Bioinformatics (Oxford, England)
|August 10, 2002
Summary
Integrating protein interaction data with mRNA expression reveals active gene subnetworks. This approach systematically uncovers regulatory pathways and generates hypotheses for gene expression changes in yeast.
Area of Science:
- Systems biology
- Genomics
- Computational biology
Background:
- Large-scale protein-protein and protein-DNA interaction databases are crucial for understanding biological processes in model organisms like yeast.
- Integrating these interaction networks with mRNA expression data offers a powerful approach to study gene regulatory dynamics.
Purpose of the Study:
- To develop a systematic, large-scale method for integrating molecular interaction networks with gene expression data.
- To identify active subnetworks that explain observed changes in gene expression.
Main Methods:
- Developed a computational approach to screen molecular interaction networks.
- Implemented a rigorous statistical measure for scoring subnetworks.
- Utilized a search algorithm to identify high-scoring subnetworks.
Main Results:
- Evaluated the method on small and large yeast networks.
- Identified significant subnetworks correlating with expression changes.
- Top-scoring subnetworks showed correspondence with known regulatory mechanisms.
Conclusions:
- Demonstrated the utility of integrating interaction and expression data for uncovering biological mechanisms.
- Showcased how large-scale genomic approaches can systematically identify signaling and regulatory pathways.