An integrated strategy for prediction uncertainty analysis

J. Vanlier, C.A. Tiemann, P.A.J. Hilbers, N.A.W. Riel, van

Research output: Contribution to journalArticleAcademicpeer-review

49 Citations (Scopus)
1 Downloads (Pure)


Motivation: 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. Availability: Source code is available at:
Original languageEnglish
Pages (from-to)1130-1135
Issue number8
Publication statusPublished - 2012


Dive into the research topics of 'An integrated strategy for prediction uncertainty analysis'. Together they form a unique fingerprint.

    van Riel, N. A. W., van Riel, N. A. W., Prompers, J. J., Vanlier, J., Tiemann, C. A., Jeneson, J. A. L., Janssens, S. M. M. & Willems van Dijk, K.


    Project: Research direct

Cite this