title: An integrated strategy for prediction uncertainty analysis.
abstract: BACKGROUND
  To further our understanding of the mechanisms underlying biochemical pathways mathematical modelling is used. Since many parameter values are unknown they need to be estimated using experimental observations. The complexity of models necessary to describe biological pathways in combination with the limited amount of quantitative data results in large parameter uncertainty which propagates into model predictions. Therefore prediction uncertainty analysis is an important topic that needs to be addressed in Systems Biology modelling.
  
  
  RESULTS
  We propose a strategy for model prediction uncertainty analysis by integrating profile likelihood analysis with Bayesian estimation. Our method is illustrated with an application to a model of the JAK-STAT signalling pathway. The analysis identified predictions on unobserved variables that could be made with a high level of confidence, despite that some parameters were non-identifiable.
  
  
  METHODS
  Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.
authors.affiliation: Department of BioMedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. j.vanlier@tue.nl
authors.forename: J
authors.initials: J
authors.lastname: Vanlier
authors.forename: C A
authors.initials: CA
authors.lastname: Tiemann
authors.forename: P A J
authors.initials: PA
authors.lastname: Hilbers
authors.forename: N A W
authors.initials: NA
authors.lastname: van Riel
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D053613:Janus Kinase 1; D016013:Likelihood Functions; D008390:Markov Chains; D008954:Models, Biological; D011381:Programming Languages; D050791:STAT Transcription Factors; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*; D035501:Uncertainty
chemical_list: D050791:STAT Transcription Factors; D053613:Janus Kinase 1