Global organization of metabolic fluxes in the bacterium Escherichia coli

Abstract

To identify the interplay between the underlying topology 1-3 of the E. coli K12 MG1655 metabolic network and its functional organization, we focus on the global features of potentially achievable flux states in this model organism with a fully sequenced and annotated genome 13,14 . In accordance with flux-balance-analysis (FBA) [8][9][10][11][12] , we first identified the solution space (i.e., all possible flux states under a given condition) using constraints imposed by the conservation of mass and the stoichiometry of the reaction system for the reconstructed E. coli metabolic network [8][9][10][11][12] . Assuming that

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

“…This result can be of practical importance for synthetic biology efforts aimed towards manipulating flux within biological systems. Furthermore, this finding was hypothesized to be a universal feature of metabolic activity in all cells and was consistent with flux measurements from 13 C labeling experiments 67 .…”

“…The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features. These features include the most probable active pathways and utilized metabolites under all possible growth conditions 67,69,73,75 , the existence of alternate optimal solutions and their physiological significance 65 , conserved intracellular pools of metabolites 68 , coupled reaction activities 66 and their relationship to gene co-expression 77 , metabolite coupling 71 , metabolite utilization 72 , the organization of metabolic networks 64, 76 , strategies for E. coli to incorporate metabolic redundancy 78 , and the dominant functional states of the network across various environments 70,74,79 . These findings are both driven by biased approaches utilizing FBA and biomass objective function optimization and by unbiased approaches such as graph-based analyses (see Fig.…”

“…Applications of the E. coli GEM range from pragmatic to theoretical studies, and can be classified into five general categories ( Fig. 3): 1) metabolic engineering [20][21][22][23][24][25][26][27][28][29][30] ; 2) biological discovery [31][32][33][34][35][36][37] ; 3) assessment of phenotypic behavior 19, ; 4) biological network analysis [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] ; and 5) studies of bacterial evolution [80][81][82] . The in silico methods used to probe the E. coli GEM in each study are summarized in Fig.…”

“…coli is generally viewed as having the most complete characterization of any model organism 98,99 . Due to the incorporation of thousands of metabolic interactions with relatively high reliability (e.g., 92% of the genes included in the latest reconstruction of E. coli 19 have experimentally determined annotated functions 99 , Table 1), validated genomescale reconstructions of E. coli have become popular resources for the analysis of various network properties [64][65][66][67][68][69][70][71][72][73][74][75][76][77][78][79] . The methods designed to analyze the underlying network structure of E. coli metabolism, some characterizing its interplay with regulation, have been developed to determine a number of physiological features.…”

“…4). One noteworthy study utilizing the GEM outlined network examined thousands of different potential growth conditions and observed a 'highflux backbone' in E. coli that both carried high levels of flux across the different environmental conditions and was composed of a relatively small set of enzymatic reactions 67 . This result can be of practical importance for synthetic biology efforts aimed towards manipulating flux within biological systems.…”