![]() Identify extreme pathways ( Schilling et al. Following this spirit, several frameworks for examining structural and topological network propertiesīased on convex analysis have been developed and demonstrated for small-scale metabolic representations (∼100 reactions) to In the postgenomic era, each cellular function, biological entity, or physiological event is seen in the context of a complex 2002) (4) assessing the performance limits of metabolic networks in response to gene additions or deletions ( Burgard and Maranas 2001) and (5) suggesting gene knockout strategies for enhancing biochemical production ( Burgard et al. 1993) (3) quantitatively predicting cellular growth rates ( Edwards et al. 2001) (2) identifying the correct sequence of byproduct secretion under increasingly anaerobic conditions ( Varma et al. Outcomes of gene knockout experiments ( Edwards and Palsson 2000 Badarinarayana et al. Specifically, stoichiometric models ofĮscherichia coli metabolism utilized within the flux balance analysis (FBA) framework have been used for (1) qualitatively predicting the Refinement of metabolic reconstructions through an iterative model-building process. Metabolic models, as well as which reactions may be under coordinated regulation, alluding to a mechanism for the continuous The output of these analyses provides significant biological insight as to which reactions are potentially missing from In fixed flux proportions under steady-state conditions) in metabolic models has attracted considerable interest in recent Of the metabolic network under steady-state conditions) and enzyme subsets (i.e., groups of reactions that operate together To this end, the identification of blocked reactions (i.e., reactions incapable of carrying flux due to the stoichiometry Interventions and ensuring the consistency of the underlying reconstructions. Principles of metabolic interactions within cellular networks, and at the practical level for more effectively focusing engineering Structural and topological properties of metabolic networks is important at both the conceptual level, to reveal the organizational These models provide a largely complete skeleton of the metabolic reactions present in an organism. Metabolic reconstructions for various microorganisms ( Covert et al. The recent abundance of complete genome sequences has enabled the generation of genome-scale ![]() Despite this flexibility, network stoichiometry and connectivity do establish limits/barriers to the coordinationĪnd accessibility of reactions. ![]() The FCF approach thus provides a novel and versatile tool forĪiding metabolic reconstructions and guiding genetic manipulations.Īn overarching attribute of metabolic networks is their inherent robustness and ability to cope with ever-changing environmentalĬonditions. Incapable of carrying flux under a certain condition equivalent knockouts, defined as the set of all possible reactions whoseĭeletion forces the flux through a particular reaction to zero and sets of affected reactions denoting all reactions whoseįluxes are forced to zero if a particular reaction is deleted. Flux coupling analysis also enables the global identification of blocked reactions, which are all reactions 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. The framework is demonstrated on genome-scale metabolic reconstructions of Helicobacter pylori, Escherichia coli, and Saccharomyces cerevisiae. In this paper, we introduce the Flux Coupling Finder (FCF) framework for elucidating the topological and flux connectivityįeatures of genome-scale metabolic networks.
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