TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm

Pey, Jon; Villar, Juan A.; Tobalina, Luis; Rezola, Alberto; García, José M.; Beasley, John E.; Planes, Francisco J.

doi:10.1093/bioinformatics/btu733

Cited by 27 publications

(19 citation statements)

References 35 publications

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“…As suggested in Pey et al (2015) , a good unit to measure the efficiency of LP methods is the number of LP problems solved for one EFM obtained. This makes efficiency as independent as possible of the software, hardware and model chosen.…”

Section: Resultsmentioning

confidence: 99%

“…Methods based on LP techniques are capable of producing sets of EFMs at a better efficiency rate, both in time and computer resources. Several efforts have been made to propose efficient algorithms that can produce large enough sets of EFMs in GSMNs ( Kaleta et al , 2009 ; Quek and Nielsen, 2014 ; Pey et al , 2015 ). The critical point of these techniques is to use different additional constraints and objective functions to transform the stoichiometric and thermodynamic feasibility constraints into an optimization problem.…”

Section: Introductionmentioning

confidence: 99%

“…It is known that the ‘ideal’ rate is to find a new EFM for each LP problem run. As far as we know, the best efficiency rate was obtained by Pey et al (2015) , where they achieved an efficiency rate of 1.3 (i.e. a new EFM was obtained for each 1.3 LP problems solved).…”

Section: Introductionmentioning

confidence: 99%

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Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

2020

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Abstract Motivation Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs. Results Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude. Availability and implementation Software is freely available at https://github.com/biogacop/Boost_LP_EFM. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

2020

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“…Optionally, relative fluxes of reactions active in an EFM, which are also computed during EFM calculation by most tools, and/or omics data, can also be used for selecting EFMs for visualization. In this study, EFMs were computed using TreeEFM [41] (see Supplementary Information for details on EFM Generation). Additionally, relative fluxes (in Use Case 1) and gene level statistics (from differential expression analysis, in Use Case 2) were used as inputs in this study.…”

Section: Inputsmentioning

confidence: 99%

EFMviz: A COBRA Toolbox Extension to Visualize Elementary Flux Modes in Genome-Scale Metabolic Models

et al. 2020

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Elementary Flux Modes (EFMs) are a tool for constraint-based modeling and metabolic network analysis. However, systematic and automated visualization of EFMs, capable of integrating various data types is still a challenge. In this study, we developed an extension for the widely adopted COBRA Toolbox, EFMviz, for analysis and graphical visualization of EFMs as networks of reactions, metabolites and genes. The analysis workflow offers a platform for EFM visualization to improve EFM interpretability by connecting COBRA toolbox with the network analysis and visualization software Cytoscape. The biological applicability of EFMviz is demonstrated in two use cases on medium (Escherichia coli, iAF1260) and large (human, Recon 2.2) genome-scale metabolic models. EFMviz is open-source and integrated into COBRA Toolbox. The analysis workflows used for the two use cases are detailed in the two tutorials provided with EFMviz along with the data used in this study.

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“…Alternative methods do not aim to find all EFMs, but limit their scope to find subsets. Subsets are selected randomly [16,17] or based on support information [18] or subject to additional constraints [19][20][21]. The question remains, however, whether or not these subsets are biologically relevant.…”

Section: Introductionmentioning

confidence: 99%

Which sets of elementary flux modes form thermodynamically feasible flux distributions?

et al. 2016

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Elementary flux modes (EFMs) are non‐decomposable steady‐state fluxes through metabolic networks. Every possible flux through a network can be described as a superposition of EFMs. The definition of EFMs is based on the stoichiometry of the network, and it has been shown previously that not all EFMs are thermodynamically feasible. These infeasible EFMs cannot contribute to a biologically meaningful flux distribution. In this work, we show that a set of thermodynamically feasible EFMs need not be thermodynamically consistent. We use first principles of thermodynamics to define the feasibility of a flux distribution and present a method to compute the largest thermodynamically consistent sets (LTCSs) of EFMs. An LTCS contains the maximum number of EFMs that can be combined to form a thermodynamically feasible flux distribution. As a case study we analyze all LTCSs found in Escherichia coli when grown on glucose and show that only one LTCS shows the required phenotypical properties. Using our method, we find that in our E. coli model < 10% of all EFMs are thermodynamically relevant.

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TreeEFM: calculating elementary flux modes using linear optimization in a tree-based algorithm

Abstract: The stand-alone software TreeEFM is implemented in C++ and interacts with the open-source linear solver COIN-OR Linear program Solver (CLP).

Cited by 27 publications

References 35 publications

Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach

EFMviz: A COBRA Toolbox Extension to Visualize Elementary Flux Modes in Genome-Scale Metabolic Models

Which sets of elementary flux modes form thermodynamically feasible flux distributions?

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