Identifiability of stochastically modelled reaction networks
SCIE
SCOPUS
- Title
- Identifiability of stochastically modelled reaction networks
- Authors
- ENCISO, GERMAN; ERBAN, RADEK; KIM, JINSU
- Date Issued
- 2021-10
- Publisher
- Cambridge University Press
- 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 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.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/110543
- DOI
- 10.1017/s0956792520000492
- ISSN
- 0956-7925
- Article Type
- Article
- Citation
- European Journal of Applied Mathematics, vol. 32, no. 5, page. 865 - 887, 2021-10
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.