File Download

There are no files associated with this item.

  • Find it @ UNIST can give you direct access to the published full text of this article. (UNISTARs only)
Related Researcher

김필원

Kim, Pilwon
Nonlinear and Complex Dynamics
Read More

Views & Downloads

Detailed Information

Cited time in webofscience Cited time in scopus
Metadata Downloads

Fast Probability Generating Function Method for Stochastic Chemical Reaction Networks

Author(s)
Kim, PilwonLee, Chang Hyeong
Issued Date
2014-01
URI
https://scholarworks.unist.ac.kr/handle/201301/3757
Fulltext
http://match.pmf.kg.ac.rs/electronic_versions/Match71/n1/match71n1_57-69.pdf
Citation
MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY, v.71, no.1, pp.57 - 69
Abstract
Chemical master equations of the stochastic reaction network can be reformulated into a partial differential equation(PDE) of a probability generating function (PGF). Such PDEs are mostly hard to deal with due to variable coefficients and lack of proper boundary conditions. In this paper, we propose a way to reduce PGF-PDEs into a sparse linear system of coefficients of a power series solution. A power of such matrix gives a fast approximation of the solution. The process can be further accelerated by truncating high-order moments. The truncation also makes the method applicable to reaction networks with time-varying reaction rates. We show numerical accuracy of the method by simulating motivating biochemical examples including a viral infection model and G(2)/M model.
Publisher
UNIV KRAGUJEVAC
ISSN
0340-6253

qrcode

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.