Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

ABSTRACT: Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with

SUBMITTER: Schillings C 

PROVIDER: S-EPMC4552555 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature