Computationally efficient flux variability analysis

Steinn Gudmundsson; Ines Thiele

doi:10.1186/1471-2105-11-489

Computationally efficient flux variability analysis

Steinn Gudmundsson
, Ines Thiele

Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild

Háskóli Íslands

Rannsóknarafurð: Framlag til fræðitímarits › Grein › ritrýni

Útdráttur

Background: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.Results: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.Conclusions: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

Upprunalegt tungumál	Enska
Númer greinar	489
Fræðitímarit	BMC Bioinformatics
Bindi	11
DOI	https://doi.org/10.1186/1471-2105-11-489
Útgáfustaða	Útgefið - 29 sep. 2010

Athugasemd

Funding Information: We want to thank the authors of GLPK, GLPKMEX and CPLEXINT for making their code publicly available. We would like to thank the anonymous reviewers for their helpful comments. The authors are also grateful to Ronan M.T. Fleming for valuable discussions. This study was supported by the Office of Science (ASCR), Department of Energy, under Award Number DE-SC00092009 ("Numerical Optimization Algorithms and Software for Systems Biology”).

Aðgangur að skjali

10.1186/1471-2105-11-489

Vitna í þetta

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title = "Computationally efficient flux variability analysis",

abstract = "Background: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.Results: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.Conclusions: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.",

author = "Steinn Gudmundsson and Ines Thiele",

note = "Funding Information: We want to thank the authors of GLPK, GLPKMEX and CPLEXINT for making their code publicly available. We would like to thank the anonymous reviewers for their helpful comments. The authors are also grateful to Ronan M.T. Fleming for valuable discussions. This study was supported by the Office of Science (ASCR), Department of Energy, under Award Number DE-SC00092009 ({"}Numerical Optimization Algorithms and Software for Systems Biology”).",

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AU - Thiele, Ines

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Y1 - 2010/9/29

N2 - Background: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.Results: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.Conclusions: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

AB - Background: Flux variability analysis is often used to determine robustness of metabolic models in various simulation conditions. However, its use has been somehow limited by the long computation time compared to other constraint-based modeling methods.Results: We present an open source implementation of flux variability analysis called fastFVA. This efficient implementation makes large-scale flux variability analysis feasible and tractable allowing more complex biological questions regarding network flexibility and robustness to be addressed.Conclusions: Networks involving thousands of biochemical reactions can be analyzed within seconds, greatly expanding the utility of flux variability analysis in systems biology.

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