title: Flux-sum analysis: a metabolite-centric approach for understanding the metabolic network.
abstract: BACKGROUND
  Constraint-based flux analysis of metabolic network model quantifies the reaction flux distribution to characterize the state of cellular metabolism. 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. Thus, it would be practical to incorporate the metabolite states into the model for the analysis of the network.
  
  
  RESULTS
  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. By doing so, the effect of varying metabolite flux-sum on physiological change can be simulated by resorting to mixed integer linear programming. From the results, we could classify various metabolite types based on the flux-sum profile. Using the iAF1260 in silico metabolic model of Escherichia coli, we demonstrated that this novel concept complements the conventional reaction-centric analysis.
  
  
  CONCLUSIONS
  Metabolite flux-sum analysis elucidates the roles of metabolites in the network. 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.
authors.affiliation: NUS Graduate School for Integrative Sciences and Engineering, National University of Singapore, 28 Medical Drive, #05-01, 117456, Singapore. g0801781@nus.edu.sg
authors.forename: Bevan Kai Sheng
authors.initials: BK
authors.lastname: Chung
authors.forename: Dong-Yup
authors.initials: DY
authors.lastname: Lee
mesh_terms: D019295:Computational Biology; D004926:Escherichia coli / Q000166:cytology / Q000378:metabolism; D007700:Kinetics; D053858:Metabolic Networks and Pathways; D055432:Metabolomics / Q000379:methods*; D008954:Models, Biological; D010641:Phenotype