A Bayesian approach to targeted experiment design.

ABSTRACT:

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

SUBMITTER: Vanlier J 

PROVIDER: S-EPMC3324513 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature