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Syntactic markovian bisimulation for chemical reaction networks

Abstract:

In chemical reaction networks (CRNs) with stochastic semantics based on continuous-time Markov chains (CTMCs), the typically large populations of species cause combinatorially large state spaces. This makes the analysis very difficult in practice and represents the major bottleneck for the applicability of minimization techniques based, for instance, on lumpability. In this paper we present syntactic Markovian bisimulation (SMB), a notion of bisimulation developed in the Larsen-Skou style of ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-319-63121-9_23

Authors


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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Oxford college:
St Anne's College
Role:
Author
ORCID:
0000-0002-8705-8488

Contributors

Role:
Editor
Role:
Editor, Editor
Role:
Editor
Role:
Editor
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Funding agency for:
Cardelli, L
Grant:
Research Professorship
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Grant:
QUANTICOL, 600708
Publisher:
Springer, Cham Publisher's website
Host title:
Models, Algorithms, Logics and Tools
Series:
Lecture Notes in Computer Science
Volume:
10460
Pages:
466-483
Publication date:
2017-07-25
DOI:
ISSN:
0302-9743
Source identifiers:
724764
ISBN:
9783319631202
Pubs id:
pubs:724764
UUID:
uuid:6b46dbcb-a2f0-4bef-8b8f-f1d8ea278129
Local pid:
pubs:724764
Deposit date:
2018-06-13

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