title: A Probabilistic Boolean Network Approach for the Analysis of Cancer-Specific Signalling: A Case Study of Deregulated PDGF Signalling in GIST.
abstract: BACKGROUND
  Signal transduction networks are increasingly studied with mathematical modelling approaches while each of them is suited for a particular problem. For the contextualisation and analysis of signalling networks with steady-state protein data, we identified probabilistic Boolean network (PBN) as a promising framework which could capture quantitative changes of molecular changes at steady-state with a minimal parameterisation.
  
  
  CONCLUSIONS
  In our case study, we successfully applied the PBN approach to model and analyse the deregulated Platelet-Derived Growth Factor (PDGF) signalling pathway in Gastrointestinal Stromal Tumour (GIST). We experimentally determined a rich and accurate dataset of steady-state profiles of selected downstream kinases of PDGF-receptor-alpha mutants in combination with inhibitor treatments. Applying the tool optPBN, we fitted a literature-derived candidate network model to the training dataset consisting of single perturbation conditions. Model analysis suggested several important crosstalk interactions. The validity of these predictions was further investigated experimentally pointing to relevant ongoing crosstalk from PI3K to MAPK signalling in tumour cells. The refined model was evaluated with a validation dataset comprising multiple perturbation conditions. The model thereby showed excellent performance allowing to quantitatively predict the combinatorial responses from the individual treatment results in this cancer setting. The established optPBN pipeline is also widely applicable to gain a better understanding of other signalling networks at steady-state in a context-specific fashion.
authors.affiliation: Systems Biology group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.
authors.forename: Panuwat
authors.identifier: http://orcid.org/0000-0002-1124-5375
authors.initials: P
authors.lastname: Trairatphisan
authors.affiliation: Systems Biology group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.
authors.forename: Monique
authors.initials: M
authors.lastname: Wiesinger
authors.affiliation: Molecular disease mechanisms group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.
authors.forename: Christelle
authors.initials: C
authors.lastname: Bahlawane
authors.affiliation: Molecular disease mechanisms group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.
authors.forename: Serge
authors.initials: S
authors.lastname: Haan
authors.affiliation: Systems Biology group, Life Sciences Research Unit, University of Luxembourg, Luxembourg, Luxembourg.
authors.forename: Thomas
authors.initials: T
authors.lastname: Sauter
mesh_terms: D019295:Computational Biology / Q000379:methods*; D046152:Gastrointestinal Stromal Tumors / Q000235:genetics / Q000473:pathology*; D006801:Humans; D020935:MAP Kinase Signaling System; D010982:Platelet-Derived Growth Factor / Q000378:metabolism*; D017354:Point Mutation; D011336:Probability; D015398:Signal Transduction
chemical_list: D010982:Platelet-Derived Growth Factor