title: Fast variance reduction for steady-state simulation and sensitivity analysis of stochastic chemical systems using shadow function estimators.
abstract: We address the problem of estimating steady-state quantities associated to systems of stochastic chemical kinetics. In most cases of interest, these systems are analytically intractable, and one has to resort to computational methods to estimate stationary values of cost functions. In this work, we introduce a novel variance reduction algorithm for stochastic chemical kinetics, inspired by related methods in queueing theory, in particular the use of shadow functions. Using two numerical examples, we demonstrate the efficiency of the method for the calculation of steady-state parametric sensitivities and evaluate its performance in comparison to other estimation methods.
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
authors.forename: Andreas
authors.initials: A
authors.lastname: Milias-Argeitis
authors.affiliation: Automatic Control Lab, ETH Zurich, Physikstrasse 3, 8092 Zurich, Switzerland.
authors.forename: John
authors.initials: J
authors.lastname: Lygeros
authors.affiliation: Department of Biosystems Science and Engineering, ETH Zurich, 4058 Basel, Switzerland.
authors.forename: Mustafa
authors.initials: M
authors.lastname: Khammash
mesh_terms: D000465:Algorithms; D055598:Chemical Phenomena; D003198:Computer Simulation; D007700:Kinetics; D008956:Models, Chemical; D013269:Stochastic Processes