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Stochastic flux analysis of chemical reaction networks

BACKGROUND: Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, en...

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Autores principales: Kahramanoğulları, Ozan, Lynch, James F
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878955/
https://www.ncbi.nlm.nih.gov/pubmed/24314153
http://dx.doi.org/10.1186/1752-0509-7-133
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author Kahramanoğulları, Ozan
Lynch, James F
author_facet Kahramanoğulları, Ozan
Lynch, James F
author_sort Kahramanoğulları, Ozan
collection PubMed
description BACKGROUND: Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. RESULTS: We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. CONCLUSIONS: We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network.
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spelling pubmed-38789552014-01-08 Stochastic flux analysis of chemical reaction networks Kahramanoğulları, Ozan Lynch, James F BMC Syst Biol Methodology Article BACKGROUND: Chemical reaction networks provide an abstraction scheme for a broad range of models in biology and ecology. The two common means for simulating these networks are the deterministic and the stochastic approaches. The traditional deterministic approach, based on differential equations, enjoys a rich set of analysis techniques, including a treatment of reaction fluxes. However, the discrete stochastic simulations, which provide advantages in some cases, lack a quantitative treatment of network fluxes. RESULTS: We describe a method for flux analysis of chemical reaction networks, where flux is given by the flow of species between reactions in stochastic simulations of the network. Extending discrete event simulation algorithms, our method constructs several data structures, and thereby reveals a variety of statistics about resource creation and consumption during the simulation. We use these structures to quantify the causal interdependence and relative importance of the reactions at arbitrary time intervals with respect to the network fluxes. This allows us to construct reduced networks that have the same flux-behavior, and compare these networks, also with respect to their time series. We demonstrate our approach on an extended example based on a published ODE model of the same network, that is, Rho GTP-binding proteins, and on other models from biology and ecology. CONCLUSIONS: We provide a fully stochastic treatment of flux analysis. As in deterministic analysis, our method delivers the network behavior in terms of species transformations. Moreover, our stochastic analysis can be applied, not only at steady state, but at arbitrary time intervals, and used to identify the flow of specific species between specific reactions. Our cases study of Rho GTP-binding proteins reveals the role played by the cyclic reverse fluxes in tuning the behavior of this network. BioMed Central 2013-12-07 /pmc/articles/PMC3878955/ /pubmed/24314153 http://dx.doi.org/10.1186/1752-0509-7-133 Text en Copyright © 2013 Kahramanoğulları and Lynch; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methodology Article
Kahramanoğulları, Ozan
Lynch, James F
Stochastic flux analysis of chemical reaction networks
title Stochastic flux analysis of chemical reaction networks
title_full Stochastic flux analysis of chemical reaction networks
title_fullStr Stochastic flux analysis of chemical reaction networks
title_full_unstemmed Stochastic flux analysis of chemical reaction networks
title_short Stochastic flux analysis of chemical reaction networks
title_sort stochastic flux analysis of chemical reaction networks
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3878955/
https://www.ncbi.nlm.nih.gov/pubmed/24314153
http://dx.doi.org/10.1186/1752-0509-7-133
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