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Error bound and simulation algorithm for piecewise deterministic approximations of stochastic reaction systems

Altintan, D. ; Ganguly, A. ; Koeppl, H. :
Error bound and simulation algorithm for piecewise deterministic approximations of stochastic reaction systems.
[Online-Edition: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7170830]
In: American Control Conference (ACC), 2015, 1-3 July 2015, Chicago. American Control Conference (ACC), 2015
[Konferenz- oder Workshop-Beitrag], (2015)

Offizielle URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7170830

Kurzbeschreibung (Abstract)

In cellular reaction systems, events often happen at different time and abundance scales. It is possible to simulate such multi-scale processes with exact stochastic simulation algorithms, but the computational cost of these algorithms is prohibitive due to the presence of high propensity reactions. This observation motivates the development of hybrid models and simulation algorithms that combine deterministic and stochastic representation of biochemical systems. Based on the random time change model we propose a hybrid model that partitions the reaction system into fast and slow reactions and represents fast reactions through ordinary differential equations (ODEs) while the Markov jump representation is retained for slow ones. Importantly, the partitioning is based on an error analysis which is the main contribution of the paper. The proposed error bound is then used to construct a dynamic partitioning algorithm. Simulation results are provided for two elementary reaction systems.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2015
Autor(en): Altintan, D. ; Ganguly, A. ; Koeppl, H.
Titel: Error bound and simulation algorithm for piecewise deterministic approximations of stochastic reaction systems
Sprache: Englisch
Kurzbeschreibung (Abstract):

In cellular reaction systems, events often happen at different time and abundance scales. It is possible to simulate such multi-scale processes with exact stochastic simulation algorithms, but the computational cost of these algorithms is prohibitive due to the presence of high propensity reactions. This observation motivates the development of hybrid models and simulation algorithms that combine deterministic and stochastic representation of biochemical systems. Based on the random time change model we propose a hybrid model that partitions the reaction system into fast and slow reactions and represents fast reactions through ordinary differential equations (ODEs) while the Markov jump representation is retained for slow ones. Importantly, the partitioning is based on an error analysis which is the main contribution of the paper. The proposed error bound is then used to construct a dynamic partitioning algorithm. Simulation results are provided for two elementary reaction systems.

Buchtitel: American Control Conference (ACC), 2015
Fachbereich(e)/-gebiet(e): Fachbereich Elektrotechnik und Informationstechnik > Bioinspirierte Kommunikationssysteme, Bioinspired Communication Systems
Fachbereich Elektrotechnik und Informationstechnik
Veranstaltungstitel: American Control Conference (ACC), 2015
Veranstaltungsort: Chicago
Veranstaltungsdatum: 1-3 July 2015
Hinterlegungsdatum: 03 Jun 2015 08:31
Offizielle URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7170830
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