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Model Decomposition and Stochastic Fragments

Petrov, T. and Ganguly, A. and Koeppl, H. (2012):
Model Decomposition and Stochastic Fragments.
In: Electronic Notes in Theoretical Computer Science, pp. 105-124, 284, [Online-Edition: http://linkinghub.elsevier.com/retrieve/pii/S157106611200019...],
[Article]

Abstract

In this paper, we discuss a method for decomposition, abstraction and reconstruction of the stochastic semantics of rule-based systems with conserved number of agents. Abstraction is induced by counting fragments instead of the species, which are the standard entities of information in molecular signaling. The rule-set can be decomposed to smaller rule-sets, so that the fragment-based dynamics of the whole rule-set is exactly a composition of species-based dynamics of smaller rule-sets. The reconstruction of the transient species-based dynamics is possible for certain initial distributions. We show that, if all the rules in a rule set are reversible, the reconstruction of the species-based dynamics is always possible at the stationary distribution. We use a case study of colloidal aggregation to demonstrate that the method can reduce the state space exponentially with respect to the standard, species-based description. © 2012 Elsevier B.V. All rights reserved.

Item Type: Article
Erschienen: 2012
Creators: Petrov, T. and Ganguly, A. and Koeppl, H.
Title: Model Decomposition and Stochastic Fragments
Language: English
Abstract:

In this paper, we discuss a method for decomposition, abstraction and reconstruction of the stochastic semantics of rule-based systems with conserved number of agents. Abstraction is induced by counting fragments instead of the species, which are the standard entities of information in molecular signaling. The rule-set can be decomposed to smaller rule-sets, so that the fragment-based dynamics of the whole rule-set is exactly a composition of species-based dynamics of smaller rule-sets. The reconstruction of the transient species-based dynamics is possible for certain initial distributions. We show that, if all the rules in a rule set are reversible, the reconstruction of the species-based dynamics is always possible at the stationary distribution. We use a case study of colloidal aggregation to demonstrate that the method can reduce the state space exponentially with respect to the standard, species-based description. © 2012 Elsevier B.V. All rights reserved.

Journal or Publication Title: Electronic Notes in Theoretical Computer Science
Volume: 284
Uncontrolled Keywords: cell signaling,continuous-time Markov chain,lumpability
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology
18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications
Date Deposited: 04 Apr 2014 12:15
Official URL: http://linkinghub.elsevier.com/retrieve/pii/S157106611200019...
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