Representativeness of a Set of Metabolic Pathways

Hidalgo, José F.; Egea, José A.; Guil, Francisco; García, José M.

doi:10.1007/978-3-319-56148-6_58

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…In order to avoid possible infeasible LP problems, in addition to just use one positive constraint, the blocked reactions are removed from the network ( Hidalgo et al , 2017 ).…”

Section: Methodsmentioning

confidence: 99%

“…It can be imposed that certain set of reactions must appear with non-zero flux (at least one of them). This kind of constraints are called positive constraints (see Acuña et al , 2009 or Hidalgo et al , 2017 ). Other times, the imposition is that a set of reactions must be absent in the final solution (negative constraints).…”

Section: Linear Programming Techniquesmentioning

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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“…In order to avoid possible infeasible LP problems, in addition to just use one positive constraint, the blocked reactions are removed from the network ( Hidalgo et al , 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Linear Programming Techniquesmentioning

confidence: 99%

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

2020

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“…Given two sets of reactions

, the restriction s are called the positive and negative constraints associated with T 1 and T 2 . These reaction sets are sometimes called positive (or negative) seeds (see Acuña et al 2009 , Hidalgo et al 2016 ).…”

Section: Introductionmentioning

confidence: 99%

On the representativeness and stability of a set of EFMs

2023

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Motivation Elementary flux modes are a well-known tool for analyzing metabolic networks. The whole set of elementary flux modes (EFMs) cannot be computed in most genome-scale networks due to their large cardinality. Therefore, different methods have been proposed to compute a smaller subset of EFMs that can be used for studying the structure of the network. These latter methods pose the problem of studying the representativeness of the calculated subset. In this article, we present a methodology to tackle this problem. Results We have introduced the concept of stability for a particular network parameter and its relation to the representativeness of the EFM extraction method studied. We have also defined several metrics to study and compare the EFM biases. We have applied these techniques to compare the relative behavior of previously proposed methods in two case studies. Furthermore, we have presented a new method for the EFM computation (PiEFM), which is more stable (less biased) than previous ones, has suitable representativeness measures, and exhibits better variability in the extracted EFMs. Availability and implementation Software and additional material are freely available at https://github.com/biogacop/PiEFM.

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Flux Coupling and the Objective Functions’ Length in EFMs

2020

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Structural analysis of constraint-based metabolic network models attempts to find the network’s properties by searching for subsets of suitable modes or Elementary Flux Modes (EFMs). One useful approach is based on Linear Program (LP) techniques, which introduce an objective function to convert the stoichiometric and thermodynamic constraints into a linear program (LP), using additional constraints to generate different nontrivial modes. This work introduces FLFS-FC (Fixed Length Function Sampling with Flux Coupling), a new approach to increase the efficiency of generation of large sets of different EFMs for the network. FLFS-FC is based on the importance of the length of the objective functions used in the associated LP problem and the imposition of additional negative constraints. Our proposal overrides some of the known drawbacks associated with the EFM extraction, such as the appearance of unfeasible problems or multiple repeated solutions arising from different LP problems.

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Representativeness of a Set of Metabolic Pathways

Cited by 3 publications

References 19 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

On the representativeness and stability of a set of EFMs

Flux Coupling and the Objective Functions’ Length in EFMs

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