Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:26581413", "kind": "science-record", "revision": 4, "content_hash": "sha256:898006f8b12df840a258c3160587bf84225b6393d401acece5770ba6e0bf4a75", "created_at": "2026-11-01T15:40:14Z", "updated_at": "2026-11-01T21:20:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.", "doi": "10.1093/bioinformatics/btv680", "pmid": "26581413", "authors": ["Martin Pirkl", "Elisabeth Hand", "Dieter Kube", "Rainer Spang"], "journal": "Bioinformatics (Oxford, England)", "year": 2016, "abstract": "MOTIVATION: Understanding the structure and interplay of cellular signalling pathways is one of the great challenges in molecular biology. Boolean Networks can infer signalling networks from observations of protein activation. In situations where it is difficult to assess protein activation directly, Nested Effect Models are an alternative. They derive the network structure indirectly from downstream effects of pathway perturbations. To date, Nested Effect Models cannot resolve signalling details like the formation of signalling complexes or the activation of proteins by multiple alternative input signals. Here we introduce Boolean Nested Effect Models (B-NEM). B-NEMs combine the use of downstream effects with the higher resolution of signalling pathway structures in Boolean Networks.\nRESULTS: We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.\nAVAILABILITY AND IMPLEMENTATION: R code is available at https://github.com/MartinFXP/B-NEM (github). The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761.\nCONTACT: martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "We randomly split the set of E-genes in half. For various settings of ζ (exponential decrease ζ∈{1,0.64,0.36,0.16,0.04,10−10,0}) we learn a network using the first half of the data (training set), and then score this network using the second independent half (test set) but without employing the complexity penalty in Equation (1). We repeat this step with 100 different random splits of E-genes and take the mean of graph size, connected S-genes (both in percent) and scores of the test sets.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26581413", "locator": "sections[15] \"4.2 Calibrating the sparseness parameter ζ\" paragraph 2", "quote": "We randomly split the set of E-genes in half. For various settings of ζ (exponential decrease ζ∈{1,0.64,0.36,0.16,0.04,10−10,0}) we learn a network using the first half of the data (training set), and then score this network using the second independent half (test set) but without employing the complexity penalty in Equation (1). We repeat this step with 100 different random splits of E-genes and take the mean of graph size, connected S-genes (both in percent) and scores of the test sets."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26581413", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "The BCR signalling dataset is available at the GEO database (http://www.ncbi.nlm.nih.gov/geo/) through accession number GSE68761."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Boolean Nested Effect Models (B-NEM) accurately reconstructed signal flows in simulated data and resolved BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:26581413", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "We show that B-NEMs accurately reconstruct signal flows in simulated data. Using B-NEM we then resolve BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines."}, {"asset": "source:pmid:26581413", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "R code is available at https://github.com/MartinFXP/B-NEM (github). 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