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Updated: Jul 4, 2026
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Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells
Published on: March 3, 2015
Identifying functional modules in protein-protein interaction networks: an integrated exact approach
Marcus T Dittrich1, Gunnar W Klau, Andreas Rosenwald
1Department of Bioinformatics, Biocenter, University of Würzburg, Am Hubland, 97074 Würzburg, Germany.
Bioinformatics (Oxford, England)
|July 1, 2008
Summary
We present an exact integer-linear programming solution to identify functional modules in protein-protein interaction networks. Our method efficiently finds optimal subnetworks, integrating expression and survival data for biological insights.
Area of Science:
- Systems Biology
- Bioinformatics
- Computational Biology
Background:
- Exponential growth in expression and protein-protein interaction (PPI) data necessitates integrated analysis.
- Identifying functional modules in PPI networks is crucial for understanding cellular functions beyond classical pathways.
- Existing heuristic approaches for detecting differentially expressed regions in PPI networks have limitations.
Purpose of the Study:
- To develop an exact algorithmic solution for identifying functional modules in large PPI networks.
- To introduce a novel, statistically interpretable scoring function for network nodes.
- To apply the method to biological data for discovering disease-associated subnetworks.
Main Methods:
- Integer-linear programming formulation connected to the prize-collecting Steiner tree problem.
- Development of a new additive node scoring function with scalability and multi-source data integration properties.
- Application to lymphoma microarray and survival data with the HPRD interaction network.
Main Results:
- The first exact solution for identifying optimal subnetworks in large PPI networks.
- Provably optimal subnetworks computed in minutes, despite NP-hardness.
- Identification of a proliferation-associated functional module in aggressive lymphoma (ABC subtype) and modules from non-malignant cells.
Conclusions:
- The developed method provides an accurate and efficient approach for functional module discovery in systems biology.
- The novel scoring function facilitates robust integration of diverse biological data.
- The findings offer new insights into lymphoma subtypes and cellular interactions.
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