2012/02/24 by Joep Vanlier, Christian Tiemann, P.A.J. Hilbers +1 · 103 citations
Decision Sciences · Biochemistry, Genetics and Molecular Biology · Mathematics · #Optimal Experimental Design Methods #Gene Regulatory Network Analysis #Statistical Methods in Clinical Trials #Computer science #Bayesian probability #Parameterized complexity #Bayesian experimental design #Source code #Sampling (signal processing) #Data mining #Approximate Bayesian computation #Software #Machine learning #Code (set theory) #Bayesian inference #Bayesian statistics #Algorithm #Artificial intelligence #Set (abstract data type)
paper · pdf · doi:10.1093/bioinformatics/bts092
published in Bioinformatics 28(8), 1136-1142 (Oxford University Press)
openalex publication_date 2012/02/24 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
MOTIVATION: Systems biology employs mathematical modelling to further our understanding of biochemical pathways. Since the amount of experimental data on which the models are parameterized is often limited, these models exhibit large uncertainty in both parameters and predictions. Statistical methods can be used to select experiments that will reduce such uncertainty in an optimal manner. However, existing methods for optimal experiment design (OED) rely on assumptions that are inappropriate when data are scarce considering model complexity. RESULTS: We have developed a novel method to perform OED for models that cope with large parameter uncertainty. We employ a Bayesian approach involving importance sampling of the posterior predictive distribution to predict the efficacy of a new measurement at reducing the uncertainty of a selected prediction. We demonstrate the method by applying it to a case where we show that specific combinations of experiments result in more precise predictions. AVAILABILITY AND IMPLEMENTATION: Source code is available at: http://bmi.bmt.tue.nl/sysbio/software/pua.html.