Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-4509
Authors: Waldherr, Steffen
Haasdonk, Bernard
Title: Efficient parametric analysis of the chemical master equation through model order reduction
Issue Date: 2012
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.source: BMC systems biology 6 (2012), Nr. 81. URL http://dx.doi.org./10.1186/1752-0509-6-81
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-80543
http://elib.uni-stuttgart.de/handle/11682/4526
http://dx.doi.org/10.18419/opus-4509
Abstract: BACKGROUND: Stochastic biochemical reaction networks are commonly modelled by the chemical master equation, and can be simulated as first order linear differential equations through a finite state projection. Due to the very high state space dimension of these equations, numerical simulations are computationally expensive. This is a particular problem for analysis tasks requiring repeated simulations for different parameter values. Such tasks are computationally expensive to the point of infeasibility with the chemical master equation.RESULTS:In this article, we apply parametric model order reduction techniques in order to construct accurate low-dimensional parametric models of the chemical master equation. These surrogate models can be used in various parametric analysis task such as identifiability analysis, parameter estimation, or sensitivity analysis. As biological examples, we consider two models for gene regulation networks, a bistable switch and a network displaying stochastic oscillations. CONCLUSIONS: The results show that the parametric model reduction yields efficient models of stochastic biochemical reaction networks, and that these models can be useful for systems biology applications involving parametric analysis problems such as parameter exploration, optimization, estimation or sensitivity analysis.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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