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Title: Identifiability of stochastically modelled reaction networks
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 underlying network structure as well as the system parameters. The accuracy of the presented network inference is investigated when given dynamical data are obtained via stochastic simulations.  more » « less
Award ID(s):
1763272
PAR ID:
10222835
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
European Journal of Applied Mathematics
ISSN:
0956-7925
Page Range / eLocation ID:
1 to 23
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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