ArticleBioinformatics and biology insights2013
Systematic verification of upstream regulators of a computable cellular proliferation network model on non-diseased lung cells using a dedicated dataset.
Article in Bioinformatics and biology insights, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Interpreting transcriptional changes using causal graphs: new methods and their practical utility on public networks.BMC bioinformatics · 2016Article
- Inferring gene targets of drugs and chemical compounds from gene expression profiles.Bioinformatics (Oxford, England) · 2016Article
- A systems biology approach reveals the dose- and time-dependent effect of primary human airway epithelium tissue culture after exposure to cigarette smoke in vitro.Bioinformatics and biology insights · 2015Article
- Causal biological network database: a comprehensive platform of causal biological network models focused on the pulmonary and vascular systems.Database : the journal of biological databases and curation · 2015Article
- An algorithm for score aggregation over causal biological networks based on random walk sampling.BMC research notes · 2014Article
- Systems toxicology: from basic research to risk assessment.Chemical research in toxicology · 2014Article
- On Crowd-verification of Biological Networks.Bioinformatics and biology insights · 2013Article
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Authors and funding
10 authors.
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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.
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