Stationary distributions via decomposition of stochastic reaction networks
Research output: Contribution to journal › Journal article › Research › peer-review
Documents
- Stationary distributions via decomposition of stochastic reaction networks
Final published version, 457 KB, PDF document
We examine reaction networks (CRNs) through their associated continuous-time Markov processes. Studying the dynamics of such networks is in general hard, both analytically and by simulation. In particular, stationary distributions of stochastic reaction networks are only known in some cases. We analyze class properties of the underlying continuous-time Markov chain of CRNs under the operation of join and examine conditions such that the form of the stationary distributions of a CRN is derived from the parts of the decomposed CRNs. The conditions can be easily checked in examples and allow recursive application. The theory developed enables sequential decomposition of the Markov processes and calculations of stationary distributions. Since the class of processes expressible through such networks is big and only few assumptions are made, the principle also applies to other stochastic models. We give examples of interest from CRN theory to highlight the decomposition.
Original language | English |
---|---|
Article number | 67 |
Journal | Journal of Mathematical Biology |
Volume | 82 |
Issue number | 7 |
ISSN | 0303-6812 |
DOIs | |
Publication status | Published - 2021 |
Bibliographical note
Publisher Copyright:
© 2021, The Author(s).
- Continuous-time Markov process, Markov process, mass-action system, product-form stationary distributions, Stochastic reaction networks
Research areas
ID: 276387842