![]() ![]() Only demonstrated the use of a quantitative measure of metabolite essentiality known as the "flux-sum" which indicates the turnover rate of a particular metabolite. On the other hand, the metabolite-centric approach towards addressing metabolite essentiality was, to date, only attempted by a handful of studies which mostly presented qualitative effects of removing metabolites from the network. These reaction-centric approaches, especially in, provided us with a quantitative understanding of the reaction essentiality in a metabolic network. ![]() Previous studies involving the application of FBA mostly dealt with gene or reaction knockouts and manipulation of reaction rates, which examined the phenotypic morphology resulting from the alteration of reaction fluxes. ![]() This analysis is intrinsically reaction-centric since reaction fluxes are the key description variables in the model formulation. Typical FBA can be conducted based on a reaction-centric approach where constraints were introduced to restrict the range of reaction flux values so as to define a feasible solution space. Based on this graphical representation, it is obvious that there can be two approaches in the analysis of the network, focusing on the flow of materials through either the nodes (metabolite-centric approach) or edges (reaction-centric approach). Metabolites and biochemical reactions in the metabolic network can be graphically represented by nodes and edges connecting the nodes respectively. Consequently, the constraint-based reconstruction and analysis (COBRA) approach provides an elegant method of characterizing and predicting cellular phenotype and metabolic states through the application of FBA which solves a linear optimization problem by assuming some form of cellular objective as the performance criterion. Furthermore, the assumption of metabolic steady-state is usually valid since the intracellular dynamics are typically much faster than extracellular dynamics and metabolite concentrations generally equilibrate in a much shorter time (in seconds) compared to the time-scale of genetic regulation (in minutes). flux balance analysis (FBA), circumvent issues related to kinetic modeling, including the lack of experimental data and the need for estimation of kinetic parameters, and provide useful information about the characteristics of the system as evident in various nonlinear dynamic analysis techniques. In addition, this metabolite perturbation analysis identifies the key metabolites, implicating practical application which is achievable through metabolite flux-sum manipulation in the areas of biotechnology and biomedical research.Ĭellular metabolism is more often than not represented and analysed based on a stoichiometric modelling framework under the stationary assumption of the metabolic network. Metabolite flux-sum analysis elucidates the roles of metabolites in the network. Using the i AF1260 in silico metabolic model of Escherichia coli, we demonstrated that this novel concept complements the conventional reaction-centric analysis. From the results, we could classify various metabolite types based on the flux-sum profile. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. Presented herein is a metabolite-centric approach of analyzing the metabolic network by including the turnover rate of metabolite, known as flux-sum, as key descriptive variable within the model formulation. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network. However, metabolites are key players in the metabolic network and the current reaction-centric approach may not account for the effect of metabolite perturbation on the cellular physiology due to the inherent limitation in model formulation. ![]() Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. ![]()
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