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Lee, Chang Hyeong
Stochastic Analysis & Simulation(SAS) Lab
Research Interests
  • Stochastic analysis/computation, epidemic modeling, biological system simulation

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Fast Probability Generating Function Method for Stochastic Chemical Reaction Networks

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dc.contributor.author Kim, Pilwon ko
dc.contributor.author Lee, Chang Hyeong ko
dc.date.available 2014-04-10T02:21:36Z -
dc.date.created 2014-01-20 ko
dc.date.issued 2014-01 ko
dc.identifier.citation MATCH-COMMUNICATIONS IN MATHEMATICAL AND IN COMPUTER CHEMISTRY, v.71, no.1, pp.57 - 69 ko
dc.identifier.issn 0340-6253 ko
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/3757 -
dc.description.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. ko
dc.description.statementofresponsibility close -
dc.language 영어 ko
dc.publisher UNIV KRAGUJEVAC ko
dc.title Fast Probability Generating Function Method for Stochastic Chemical Reaction Networks ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-84898724712 ko
dc.identifier.wosid 000331779000005 ko
dc.type.rims ART ko
dc.description.wostc 0 *
dc.description.scopustc 0 *
dc.date.tcdate 2015-02-28 *
dc.date.scptcdate 2014-10-13 *
dc.identifier.url http://match.pmf.kg.ac.rs/electronic_versions/Match71/n1/match71n1_57-69.pdf ko
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