Type
ArticleDate
2015-06-30Online Publication Date
2015-06-30Print Publication Date
2015-06-28Permanent link to this record
http://hdl.handle.net/10754/558859
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This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.Citation
Variance decomposition in stochastic simulators 2015, 142 (24):244115 The Journal of Chemical PhysicsPublisher
AIP PublishingJournal
The Journal of Chemical PhysicsPubMed ID
26133418ae974a485f413a2113503eed53cd6c53
10.1063/1.4922922
Scopus Count
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