Cargando…

The relationship between stochastic and deterministic quasi-steady state approximations

BACKGROUND: The quasi steady-state approximation (QSSA) is frequently used to reduce deterministic models of biochemical networks. The resulting equations provide a simplified description of the network in terms of non-elementary reaction functions (e.g. Hill functions). Such deterministic reduction...

Descripción completa

Detalles Bibliográficos
Autores principales: Kim, Jae Kyoung, Josić, Krešimir, Bennett, Matthew R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657384/
https://www.ncbi.nlm.nih.gov/pubmed/26597159
http://dx.doi.org/10.1186/s12918-015-0218-3
_version_ 1782402390169747456
author Kim, Jae Kyoung
Josić, Krešimir
Bennett, Matthew R.
author_facet Kim, Jae Kyoung
Josić, Krešimir
Bennett, Matthew R.
author_sort Kim, Jae Kyoung
collection PubMed
description BACKGROUND: The quasi steady-state approximation (QSSA) is frequently used to reduce deterministic models of biochemical networks. The resulting equations provide a simplified description of the network in terms of non-elementary reaction functions (e.g. Hill functions). Such deterministic reductions are frequently a basis for heuristic stochastic models in which non-elementary reaction functions are used to define reaction propensities. Despite their popularity, it remains unclear when such stochastic reductions are valid. It is frequently assumed that the stochastic reduction can be trusted whenever its deterministic counterpart is accurate. However, a number of recent examples show that this is not necessarily the case. RESULTS: Here we explain the origin of these discrepancies, and demonstrate a clear relationship between the accuracy of the deterministic and the stochastic QSSA for examples widely used in biological systems. With an analysis of a two-state promoter model, and numerical simulations for a variety of other models, we find that the stochastic QSSA is accurate whenever its deterministic counterpart provides an accurate approximation over a range of initial conditions which cover the likely fluctuations from the quasi steady-state (QSS). We conjecture that this relationship provides a simple and computationally inexpensive way to test the accuracy of reduced stochastic models using deterministic simulations. CONCLUSIONS: The stochastic QSSA is one of the most popular multi-scale stochastic simulation methods. While the use of QSSA, and the resulting non-elementary functions has been justified in the deterministic case, it is not clear when their stochastic counterparts are accurate. In this study, we show how the accuracy of the stochastic QSSA can be tested using their deterministic counterparts providing a concrete method to test when non-elementary rate functions can be used in stochastic simulations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0218-3) contains supplementary material, which is available to authorized users.
format Online
Article
Text
id pubmed-4657384
institution National Center for Biotechnology Information
language English
publishDate 2015
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-46573842015-11-25 The relationship between stochastic and deterministic quasi-steady state approximations Kim, Jae Kyoung Josić, Krešimir Bennett, Matthew R. BMC Syst Biol Research Article BACKGROUND: The quasi steady-state approximation (QSSA) is frequently used to reduce deterministic models of biochemical networks. The resulting equations provide a simplified description of the network in terms of non-elementary reaction functions (e.g. Hill functions). Such deterministic reductions are frequently a basis for heuristic stochastic models in which non-elementary reaction functions are used to define reaction propensities. Despite their popularity, it remains unclear when such stochastic reductions are valid. It is frequently assumed that the stochastic reduction can be trusted whenever its deterministic counterpart is accurate. However, a number of recent examples show that this is not necessarily the case. RESULTS: Here we explain the origin of these discrepancies, and demonstrate a clear relationship between the accuracy of the deterministic and the stochastic QSSA for examples widely used in biological systems. With an analysis of a two-state promoter model, and numerical simulations for a variety of other models, we find that the stochastic QSSA is accurate whenever its deterministic counterpart provides an accurate approximation over a range of initial conditions which cover the likely fluctuations from the quasi steady-state (QSS). We conjecture that this relationship provides a simple and computationally inexpensive way to test the accuracy of reduced stochastic models using deterministic simulations. CONCLUSIONS: The stochastic QSSA is one of the most popular multi-scale stochastic simulation methods. While the use of QSSA, and the resulting non-elementary functions has been justified in the deterministic case, it is not clear when their stochastic counterparts are accurate. In this study, we show how the accuracy of the stochastic QSSA can be tested using their deterministic counterparts providing a concrete method to test when non-elementary rate functions can be used in stochastic simulations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0218-3) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-23 /pmc/articles/PMC4657384/ /pubmed/26597159 http://dx.doi.org/10.1186/s12918-015-0218-3 Text en © Kim et al. 2015 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Kim, Jae Kyoung
Josić, Krešimir
Bennett, Matthew R.
The relationship between stochastic and deterministic quasi-steady state approximations
title The relationship between stochastic and deterministic quasi-steady state approximations
title_full The relationship between stochastic and deterministic quasi-steady state approximations
title_fullStr The relationship between stochastic and deterministic quasi-steady state approximations
title_full_unstemmed The relationship between stochastic and deterministic quasi-steady state approximations
title_short The relationship between stochastic and deterministic quasi-steady state approximations
title_sort relationship between stochastic and deterministic quasi-steady state approximations
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4657384/
https://www.ncbi.nlm.nih.gov/pubmed/26597159
http://dx.doi.org/10.1186/s12918-015-0218-3
work_keys_str_mv AT kimjaekyoung therelationshipbetweenstochasticanddeterministicquasisteadystateapproximations
AT josickresimir therelationshipbetweenstochasticanddeterministicquasisteadystateapproximations
AT bennettmatthewr therelationshipbetweenstochasticanddeterministicquasisteadystateapproximations
AT kimjaekyoung relationshipbetweenstochasticanddeterministicquasisteadystateapproximations
AT josickresimir relationshipbetweenstochasticanddeterministicquasisteadystateapproximations
AT bennettmatthewr relationshipbetweenstochasticanddeterministicquasisteadystateapproximations