Prediction uncertainty assessment of a systems biology model requires a sample of the full probability distribution of its parameters.

ABSTRACT: Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously among different predictions. Statistical approximations of parameter uncertainty may lead to dramatic errors in prediction uncertainty estimation. We argue that prediction uncertainty assessment must therefore be performed on a per-prediction basis using a full computational uncertainty analysis. In practice this is feasible by providing a model with a sample or ensemble representing the distribution

SUBMITTER: van Mourik S 

PROVIDER: S-EPMC4081139 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature