On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure

Deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal dis...

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Main Authors: Michail Vlysidis, Yiannis N. Kaznessis
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Entropy
Subjects:
Online Access:http://www.mdpi.com/1099-4300/20/9/678
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spelling doaj-befa19cbfde845daac43f71c31be69ea2020-11-24T21:04:42ZengMDPI AGEntropy1099-43002018-09-0120967810.3390/e20090678e20090678On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-ClosureMichail Vlysidis0Yiannis N. Kaznessis1Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USADepartment of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, MN 55455, USADeterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal distribution when the system size increases, even for kinetic constant values that result in two distinct stable solutions in the deterministic Schlögl model. Using zero-information (ZI) closure scheme, an algorithm for solving chemical master equations, we compute stationary probability distributions for varying system sizes of the Schlögl model. With ZI-closure, system sizes can be studied that have been previously unattainable by stochastic simulation algorithms. We observe and quantify paradoxical discrepancies between stochastic and deterministic models and explain this behavior by postulating that the entropy of non-equilibrium steady states (NESS) is maximum.http://www.mdpi.com/1099-4300/20/9/678closure schememaximum entropynon-equilibrium steady stateSchlögl
collection DOAJ
language English
format Article
sources DOAJ
author Michail Vlysidis
Yiannis N. Kaznessis
spellingShingle Michail Vlysidis
Yiannis N. Kaznessis
On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
Entropy
closure scheme
maximum entropy
non-equilibrium steady state
Schlögl
author_facet Michail Vlysidis
Yiannis N. Kaznessis
author_sort Michail Vlysidis
title On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
title_short On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
title_full On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
title_fullStr On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
title_full_unstemmed On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure
title_sort on differences between deterministic and stochastic models of chemical reactions: schlögl solved with zi-closure
publisher MDPI AG
series Entropy
issn 1099-4300
publishDate 2018-09-01
description Deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal distribution when the system size increases, even for kinetic constant values that result in two distinct stable solutions in the deterministic Schlögl model. Using zero-information (ZI) closure scheme, an algorithm for solving chemical master equations, we compute stationary probability distributions for varying system sizes of the Schlögl model. With ZI-closure, system sizes can be studied that have been previously unattainable by stochastic simulation algorithms. We observe and quantify paradoxical discrepancies between stochastic and deterministic models and explain this behavior by postulating that the entropy of non-equilibrium steady states (NESS) is maximum.
topic closure scheme
maximum entropy
non-equilibrium steady state
Schlögl
url http://www.mdpi.com/1099-4300/20/9/678
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