Flux coupling analysis of genome-scale metabolic network reconstructions.

By Anthony P. Burgard, Evgeni V. Nikolaev, C. Schilling, Costas D. Maranas. Genome Research, 2004

402 citations recorded by the source corpus.

In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivity features of genome-scale metabolic networks. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. The analysis allows one to determine whether any two metabolic fluxes, v(1) and v(2), are (1) directionally coupled, if a non-zero flux for v(1) implies a non-zero flux for v(2) but not necessarily the reverse; (2) partially coupled, if a non-zero flux for v(1) implies a non-zero, though variable, flux for v(2) and vice versa; or (3) fully coupled, if a non-zero flux for v(1) implies not only a non-zero but also a fixed flux for v(2) and vice versa. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions incapable of carrying flux under a certain condition; equivalent knockouts, defined as the set of all possible reactions whose deletion forces the flux through a particular reaction to zero; and sets of affected reactions denoting all reactions whose fluxes are forced to zero if a particular reaction is deleted. The FCF approach thus provides a novel and versatile tool for aiding metabolic reconstructions and guiding genetic manipulations.

Subfields: Biomedical Engineering, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/be43e728420ce85c03350ffbd1ec5d6b59e3ecf8

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Citations in the corpus

Genome-scale models of bacterial metabolism: reconstruction and applications (2008, methodology): “One of them, ﬂux coupling analysis, has become a popular analytical tool (Burgard et al ., 2004).”
Genome-scale models of microbial cells: evaluating the consequences of constraints (2004, methodology): “…v i implies a non-zero flux for v j but not necessarily the reverse;partially coupled — if a non-zero flux for v i implies a non-zero,but variable,flux for v j and vice versa; or fully coupled — if a non-zero flux for v i implies not only a non-zero but also a fixed flux for v j and vice versa 64 .”
Flux balance analysis of biological systems: applications and challenges (2009, background): “coupling that can exist between fluxes in GSMNs has been proposed Maranas and co-workers [50].”
Applications of genome-scale metabolic reconstructions (2009, methodology): “The gap-analysis step is also crucial for conversion of a genome-scale reconstruction as a knowledge base into the metabolic GENRE as a functional model, toward whose analysis the full suite of network tools can be applied”
Flux balance analysis in the era of metabolomics (2006, background): “One such approach, called flux coupling analysis, characterizes interdependency between reaction fluxes by sequentially maximizing all the fluxes normalized to a particular flux [41].”