Peer-Reviewed Publications

      Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset

      Belcastro, V.; Poussin, C.; Gebel, S.; Mathis, C.; Schlage, W. K.; Lichtner, R. B.; Quadt-Humme, S.; Wagner, S.; Hoeng, J.; Peitsch, M. C.
      Published
      Jan 1, 2013
      DOI
      10.4137/BBI.S12167
      PMID
      23926424
      Link to publication
      Topic
      Summary

      We recently constructed a computable cell proliferation network (CPN) model focused on lung tissue to unravel complex biological processes and their exposure-related perturbations from molecular profiling data. The CPN consists of edges and nodes representing upstream controllers of gene expression largely generated from transcriptomics datasets using Reverse Causal Reasoning (RCR). Here, we report an approach to biologically verify the correctness of upstream controller nodes using a specifically designed, independent lung cell proliferation dataset. Normal human bronchial epithelial cells were arrested at G1/S with a cell cycle inhibitor. Gene expression changes and cell proliferation were captured at different time points after release from inhibition. Gene set enrichment analysis demonstrated cell cycle response specificity via an overrepresentation of proliferation related gene sets. Coverage analysis of RCR-derived hypotheses returned statistical significance for cell cycle response specificity across the whole model as well as for the Growth Factor and Cell Cycle sub-network models.

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