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Identifiability of stochastically modelled reaction networks

Abstract:

Chemical reaction networks describe interactions between biochemical species. Once an underlying reaction network is given for a biochemical system, the system dynamics can be modelled with various mathematical frameworks such as continuous-time Markov processes. In this manuscript, the identifiability of the underlying network structure with a given stochastic system dynamics is studied. It is shown that some data types related to the associated stochastic dynamics can uniquely identify the ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1017/S0956792520000492

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Institution:
University of Oxford
Department:
MATHEMATICAL INSTITUTE
Sub department:
Mathematical Institute
Role:
Author
ORCID:
0000-0001-8470-3763
Publisher:
Cambridge University Press
Journal:
European Journal of Applied Mathematics More from this journal
Volume:
32
Issue:
5
Pages:
865 - 887
Publication date:
2021-02-15
Acceptance date:
2020-12-24
DOI:
EISSN:
1469-4425
ISSN:
0956-7925
Language:
English
Keywords:
Pubs id:
1109602
Local pid:
pubs:1109602
Deposit date:
2020-12-29

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