Variance decomposition in stochastic simulators

Handle URI:
http://hdl.handle.net/10754/558859
Title:
Variance decomposition in stochastic simulators
Authors:
Le Maître, O. P.; Knio, O. M.; Moraes, Alvaro ( 0000-0003-4144-1243 )
Abstract:
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Variance decomposition in stochastic simulators 2015, 142 (24):244115 The Journal of Chemical Physics
Publisher:
AIP Publishing
Journal:
The Journal of Chemical Physics
Issue Date:
28-Jun-2015
DOI:
10.1063/1.4922922
Type:
Article
ISSN:
0021-9606; 1089-7690
Additional Links:
http://scitation.aip.org/content/aip/journal/jcp/142/24/10.1063/1.4922922
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorLe Maître, O. P.en
dc.contributor.authorKnio, O. M.en
dc.contributor.authorMoraes, Alvaroen
dc.date.accessioned2015-07-05T12:35:21Zen
dc.date.available2015-07-05T12:35:21Zen
dc.date.issued2015-06-28en
dc.identifier.citationVariance decomposition in stochastic simulators 2015, 142 (24):244115 The Journal of Chemical Physicsen
dc.identifier.issn0021-9606en
dc.identifier.issn1089-7690en
dc.identifier.doi10.1063/1.4922922en
dc.identifier.urihttp://hdl.handle.net/10754/558859en
dc.description.abstractThis 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.en
dc.publisherAIP Publishingen
dc.relation.urlhttp://scitation.aip.org/content/aip/journal/jcp/142/24/10.1063/1.4922922en
dc.rightsArchived with thanks to The Journal of Chemical Physicsen
dc.titleVariance decomposition in stochastic simulatorsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalThe Journal of Chemical Physicsen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionLIMSI-CNRS, UPR 3251, Orsay, Franceen
dc.contributor.institutionDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708, USAen
kaust.authorMoraes, A.en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.