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FlexFlux: combining metabolic flux and regulatory network analyses

BACKGROUND: Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, prot...

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Autores principales: Marmiesse, Lucas, Peyraud, Rémi, Cottret, Ludovic
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4678642/
https://www.ncbi.nlm.nih.gov/pubmed/26666757
http://dx.doi.org/10.1186/s12918-015-0238-z
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author Marmiesse, Lucas
Peyraud, Rémi
Cottret, Ludovic
author_facet Marmiesse, Lucas
Peyraud, Rémi
Cottret, Ludovic
author_sort Marmiesse, Lucas
collection PubMed
description BACKGROUND: Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network. Regarding regulatory networks, a broad spectrum of methods are dedicated to their analysis although logical modelling remains the major method to take charge of large-scale networks. RESULTS: We present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package (“qual”) extension (SBML-qual) file formats and provides a set of FBA based functions. CONCLUSIONS: FlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo.toulouse.inra.fr/flexflux/. It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0238-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-46786422015-12-16 FlexFlux: combining metabolic flux and regulatory network analyses Marmiesse, Lucas Peyraud, Rémi Cottret, Ludovic BMC Syst Biol Software BACKGROUND: Expression of cell phenotypes highly depends on metabolism that supplies matter and energy. To achieve proper utilisation of the different metabolic pathways, metabolism is tightly regulated by a complex regulatory network composed of diverse biological entities (genes, transcripts, proteins, signalling molecules…). The integrated analysis of both regulatory and metabolic networks appears very insightful but is not straightforward because of the distinct characteristics of both networks. The classical method used for metabolic flux analysis is Flux Balance Analysis (FBA), which is constraint-based and relies on the assumption of steady-state metabolite concentrations throughout the network. Regarding regulatory networks, a broad spectrum of methods are dedicated to their analysis although logical modelling remains the major method to take charge of large-scale networks. RESULTS: We present FlexFlux, an application implementing a new way to combine the analysis of both metabolic and regulatory networks, based on simulations that do not require kinetic parameters and can be applied to genome-scale networks. FlexFlux is based on seeking regulatory network steady-states by performing synchronous updates of multi-state qualitative initial values. FlexFlux is then able to use the calculated steady-state values as constraints for metabolic flux analyses using FBA. As input, FlexFlux uses the standards Systems Biology Markup Language (SBML) and SBML Qualitative Models Package (“qual”) extension (SBML-qual) file formats and provides a set of FBA based functions. CONCLUSIONS: FlexFlux is an open-source java software with executables and full documentation available online at http://lipm-bioinfo.toulouse.inra.fr/flexflux/. It can be defined as a research tool that enables a better understanding of both regulatory and metabolic networks based on steady-state simulations. FlexFlux integrates well in the flux analysis ecosystem thanks to the support of standard file formats and can thus be used as a complementary tool to existing software featuring other types of analyses. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12918-015-0238-z) contains supplementary material, which is available to authorized users. BioMed Central 2015-12-15 /pmc/articles/PMC4678642/ /pubmed/26666757 http://dx.doi.org/10.1186/s12918-015-0238-z Text en © Marmiesse 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 Software
Marmiesse, Lucas
Peyraud, Rémi
Cottret, Ludovic
FlexFlux: combining metabolic flux and regulatory network analyses
title FlexFlux: combining metabolic flux and regulatory network analyses
title_full FlexFlux: combining metabolic flux and regulatory network analyses
title_fullStr FlexFlux: combining metabolic flux and regulatory network analyses
title_full_unstemmed FlexFlux: combining metabolic flux and regulatory network analyses
title_short FlexFlux: combining metabolic flux and regulatory network analyses
title_sort flexflux: combining metabolic flux and regulatory network analyses
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4678642/
https://www.ncbi.nlm.nih.gov/pubmed/26666757
http://dx.doi.org/10.1186/s12918-015-0238-z
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