Paper Info
Title
An integrated strategy for prediction uncertainty analysis.
Abstract
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.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.Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.
Year
DOI
Venue
2012
10.1093/bioinformatics/bts088
Bioinformatics
Keywords
Field
DocType
large parameter uncertainty,model prediction uncertainty analysis,profile likelihood analysis,prediction uncertainty analysis,integrated strategy,quantitative data result,nl supplementary information,systems biology modelling,parameter value,mathematical modelling,model prediction,programming languages,uncertainty,markov chains,algorithms,janus kinase 1,systems biology,bayes theorem,signal transduction
Data mining,Source code,Computer science,Software,Artificial intelligence,Confidence interval,Bayes estimator,Bayes' theorem,Markov chain,Systems biology,Uncertainty analysis,Bioinformatics,Machine learning
Journal
Volume
Issue
ISSN
28
8
1367-4811
Citations 
PageRank 
References 
13
1.05
8
Authors
4
Name
Order
Citations
PageRank
J Vanlier1352.83
Christian A. Tiemann2584.52
P A J Hilbers3131.05
N A W van Riel4342.76