Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.

ABSTRACT: Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we d

SUBMITTER: Arampatzis G 

PROVIDER: S-EPMC4498611 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature