Published in
SAGE Publications, Bioinformatics and Biology Insights, (7), p. BBI.S12167, 2013
DOI: 10.4137/bbi.s12167
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- ORCID
- SAGE Publications | PDF
- ORCID
- [www.ncbi.nlm.nih.gov] | PDF
- [www.la-press.com] | PDF
- [insights.sagepub.com] | PDF
- [europepmc.org] | PDF
- [www.researchgate.net] | PDF
- [www.ncbi.nlm.nih.gov] | PDF
- [www.ncbi.nlm.nih.gov] | PDF
Tools
Systematic Verification of Upstream Regulators of a Computable Cellular Proliferation Network Model on Non-Diseased Lung Cells Using a Dedicated Dataset
,
Carine Poussin,
Stephan Gebel,
Carole Mathis,
Rosemarie B. Lichtner,
Walter K. Schlage,
Sibille Quadt-Humme,
Sandra Wagner,
Julia Hoeng,
Manuel C. Peitsch
Full text: Download
Abstract
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.