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On Projection-Based Model Reduction of Biochemical Networks Part I: The Deterministic Case
Sootla, Aivar; Anderson, James
2014In Proceedings of the 53rd IEEE Conference on Decision and Control
Peer reviewed
 

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Keywords :
model reduction; biochemical reaction networks; linear noise approximation
Abstract :
[en] This paper addresses the problem of model reduction for dynamical system models that describe biochemical reaction networks. Inherent in such models are properties such as stability, positivity and network structure. Ideally these properties should be preserved by model reduction procedures, although traditional projection based approaches struggle to do this. We propose a projection based model reduction algorithm which uses generalised block diagonal Gramians to preserve structure and positivity. Two algorithms are presented, one provides more accurate reduced order models, the second provides easier to simulate reduced order models.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Sootla, Aivar ;  Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Smart grids
Anderson, James;  University of Oxford
Language :
English
Title :
On Projection-Based Model Reduction of Biochemical Networks Part I: The Deterministic Case
Publication date :
December 2014
Event name :
53rd IEEE Conference on Decision and Control
Event date :
December 15-17, 2014
Audience :
International
Main work title :
Proceedings of the 53rd IEEE Conference on Decision and Control
Peer reviewed :
Peer reviewed
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since 23 February 2015

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