Global sensitivity analysis in stochastic simulators of uncertain reaction networks.


Journal Article

Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

Full Text

Duke Authors

Cited Authors

  • Navarro Jimenez, M; Le Maître, OP; Knio, OM

Published Date

  • December 2016

Published In

Volume / Issue

  • 145 / 24

Start / End Page

  • 244106 -

PubMed ID

  • 28049312

Pubmed Central ID

  • 28049312

Electronic International Standard Serial Number (EISSN)

  • 1089-7690

International Standard Serial Number (ISSN)

  • 0021-9606

Digital Object Identifier (DOI)

  • 10.1063/1.4971797


  • eng