FFCA: a feasibility-based method for flux coupling analysis of metabolic networks

Dávid, László; Marashi, Sayed-Amir; Larhlimi, Abdelhalim; Mieth, Bettina; Bockmayr, Alexander

doi:10.1186/1471-2105-12-236

Cited by 24 publications

(21 citation statements)

References 29 publications

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“…For the platelet we focus on identification of perfectly correlated reaction sets (which simplify the network by collapsing reactions into modules), but also consider their interactions with other reactions that are coupled to them222338. iAT-PLT-636 contains 1008 reactions and transporters with a 289 dimension (right) null space, indicating of a richness of metabolic capabilities.…”

Section: Discussionmentioning

confidence: 99%

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

Thomas

Rahmanian

Bordbar

et al. 2014

Sci Rep

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Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

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Section: Discussionmentioning

confidence: 99%

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

Thomas

Rahmanian

Bordbar

et al. 2014

Sci Rep

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“…Second, we use inference rules to minimize the number of linear programming problems that have to be solved. We prove the efficiency of our algorithm by successfully competing with the most recent approach [31]. We show that FCA can now be quickly performed even for very large genome-scale metabolic networks.…”

Section: Introductionmentioning

confidence: 89%

F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

et al. 2012

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BackgroundFlux coupling analysis (FCA) has become a useful tool in the constraint-based analysis of genome-scale metabolic networks. FCA allows detecting dependencies between reaction fluxes of metabolic networks at steady-state. On the one hand, this can help in the curation of reconstructed metabolic networks by verifying whether the coupling between reactions is in agreement with the experimental findings. On the other hand, FCA can aid in defining intervention strategies to knock out target reactions.ResultsWe present a new method F2C2 for FCA, which is orders of magnitude faster than previous approaches. As a consequence, FCA of genome-scale metabolic networks can now be performed in a routine manner.ConclusionsWe propose F2C2 as a fast tool for the computation of flux coupling in genome-scale metabolic networks. F2C2 is freely available for non-commercial use at https://sourceforge.net/projects/f2c2/files/.

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“…Due to a number of theoretical and algorithmic improvements [12,24,25], it is now possible to perform FCA on large genome-scale metabolic network reconstructions in a few minutes of computation time on a standard desktop computer.…”

Section: Introductionmentioning

confidence: 99%

Generic flux coupling analysis

Reimers

Goldstein

Bockmayr

2015

Mathematical Biosciences

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a b s t r a c tFlux coupling analysis (FCA) has become a useful tool for aiding metabolic reconstructions and guiding genetic manipulations. Originally, it was introduced for constraint-based models of metabolic networks that are based on the steady-state assumption. Recently, we have shown that the steady-state assumption can be replaced by a weaker lattice-theoretic property related to the supports of metabolic fluxes. In this paper, we further extend our approach and develop an efficient algorithm for generic flux coupling analysis that works with any kind of qualitative pathway model. We illustrate our method by thermodynamic flux coupling analysis (tFCA), which allows studying steady-state metabolic models with loop-law thermodynamic constraints. These models do not satisfy the lattice-theoretic properties required in our previous work. For a selection of genome-scale metabolic network reconstructions, we discuss both theoretically and practically, how thermodynamic constraints strengthen the coupling results that can be obtained with classical FCA.A prototype implementation of tFCA is available at http://hoverboard.io/L4FC.

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FFCA: a feasibility-based method for flux coupling analysis of metabolic networks

Cited by 24 publications

References 29 publications

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

F2C2: a fast tool for the computation of flux coupling in genome-scale metabolic networks

Generic flux coupling analysis

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