Petrov, Tatjana ; Ganguly, Arnab ; Koeppl, Heinz (2024)
Model Decomposition and Stochastic Fragments.
In: Electronic Notes in Theoretical Computer Science, 2012, 284
doi: 10.26083/tuprints-00026720
Article, Secondary publication, Publisher's Version
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Item Type: | Article |
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Type of entry: | Secondary publication |
Title: | Model Decomposition and Stochastic Fragments |
Language: | English |
Date: | 30 April 2024 |
Place of Publication: | Darmstadt |
Year of primary publication: | 20 June 2012 |
Place of primary publication: | Amsterdam |
Publisher: | Elsevier |
Journal or Publication Title: | Electronic Notes in Theoretical Computer Science |
Volume of the journal: | 284 |
DOI: | 10.26083/tuprints-00026720 |
Corresponding Links: | |
Origin: | Secondary publication service |
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. |
Uncontrolled Keywords: | cell signaling, continuous-time Markov chain, lumpability |
Status: | Publisher's Version |
URN: | urn:nbn:de:tuda-tuprints-267207 |
Classification DDC: | 500 Science and mathematics > 570 Life sciences, biology 600 Technology, medicine, applied sciences > 621.3 Electrical engineering, electronics |
Date Deposited: | 30 Apr 2024 09:12 |
Last Modified: | 02 Aug 2024 08:12 |
URI: | https://tuprints.ulb.tu-darmstadt.de/id/eprint/26720 |
PPN: | 520261003 |
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