title: Analyzing synergistic and non-synergistic interactions in signalling pathways using Boolean Nested Effect Models.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  martin-franz-xaver.pirkl@ukr.de, Rainer.Spang@ukr.de
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and.
authors.forename: Martin
authors.initials: M
authors.lastname: Pirkl
authors.affiliation: Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen.
authors.forename: Elisabeth
authors.initials: E
authors.lastname: Hand
authors.affiliation: Department of Haematology and Oncology, University Medical Centre of the Georg-August University of Göttingen, 37073 Göttingen.
authors.forename: Dieter
authors.initials: D
authors.lastname: Kube
authors.affiliation: Statistical Bioinformatics Department, Institute of Functional Genomics, University of Regensburg, 93053 Regensburg and.
authors.forename: Rainer
authors.initials: R
authors.lastname: Spang
mesh_terms: D016208:Databases, Factual; D008962:Models, Theoretical; D019869:Phosphatidylinositol 3-Kinases; D011506:Proteins; D015398:Signal Transduction
chemical_list: D011506:Proteins; D019869:Phosphatidylinositol 3-Kinases