An integer linear programming approach for finding deregulated subgraphs in regulatory networks.

An integer linear programming approach for finding deregulated subgraphs in regulatory networks. Backes, C., Rurainski, A., Klau, G. W, Müller, O., Stöckel, D., Gerasch, A., Küntzer, J., Maisel, D., Ludwig, N., Hein, M., Keller, A., Burtscher, H., Kaufmann, M., Meese, E., & Lenhof, H. Nucleic acids research, 40:e43, March, 2012.
doi abstract bibtex

Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players.

@Article{Backes2012,
  author          = {Backes, Christina and Rurainski, Alexander and Klau, Gunnar W and Müller, Oliver and Stöckel, Daniel and Gerasch, Andreas and Küntzer, Jan and Maisel, Daniela and Ludwig, Nicole and Hein, Matthias and Keller, Andreas and Burtscher, Helmut and Kaufmann, Michael and Meese, Eckart and Lenhof, Hans-Peter},
  title           = {An integer linear programming approach for finding deregulated subgraphs in regulatory networks.},
  journal         = {Nucleic acids research},
  year            = {2012},
  volume          = {40},
  pages           = {e43},
  month           = mar,
  issn            = {1362-4962},
  abstract        = {Deregulation of cell signaling pathways plays a crucial role in the development of tumors. The identification of such pathways requires effective analysis tools that facilitate the interpretation of expression differences. Here, we present a novel and highly efficient method for identifying deregulated subnetworks in a regulatory network. Given a score for each node that measures the degree of deregulation of the corresponding gene or protein, the algorithm computes the heaviest connected subnetwork of a specified size reachable from a designated root node. This root node can be interpreted as a molecular key player responsible for the observed deregulation. To demonstrate the potential of our approach, we analyzed three gene expression data sets. In one scenario, we compared expression profiles of non-malignant primary mammary epithelial cells derived from BRCA1 mutation carriers and of epithelial cells without BRCA1 mutation. Our results suggest that oxidative stress plays an important role in epithelial cells of BRCA1 mutation carriers and that the activation of stress proteins may result in avoidance of apoptosis leading to an increased overall survival of cells with genetic alterations. In summary, our approach opens new avenues for the elucidation of pathogenic mechanisms and for the detection of molecular key players.},
  citation-subset = {IM},
  completed       = {2012-05-29},
  country         = {England},
  doi             = {10.1093/nar/gkr1227},
  issn-linking    = {0305-1048},
  issue           = {6},
  keywords        = {Adenocarcinoma, genetics, metabolism; Algorithms; Breast, cytology, metabolism; Cell Line, Tumor; Colorectal Neoplasms, genetics, metabolism; Epithelial Cells, metabolism; Female; Gene Expression Profiling; Gene Expression Regulation; Gene Regulatory Networks; Genes, BRCA1; Glioma, genetics, metabolism; Humans; Mutation; Programming, Linear; Protein Interaction Maps; Signal Transduction},
  nlm-id          = {0411011},
  owner           = {NLM},
  pii             = {gkr1227},
  pmc             = {PMC3315310},
  pmid            = {22210863},
  pubmodel        = {Print-Electronic},
  pubstatus       = {ppublish},
  revised         = {2015-01-29},
}

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