Robustness Analysis of the Escherichia coli Metabolic Network

Abstract

Genomic, biochemical, and strain-specific data can be assembled to define an in silico representation of the metabolic network for a select group of single cellular organisms. Flux-balance analysis and phenotypic phase planes derived therefrom have been developed and applied to analyze the metabolic capabilities and characteristics of Escherichia coli K-12. These analyses have shown the existence of seven essential reactions in the central metabolic pathways (glycolysis, pentose phosphate pathway, tricarboxylic acid cycle) for the growth in glucose minimal media. The corresponding seven gene products can be grouped into three categories: (1) pentose phosphate pathway genes, (2) three-carbon glycolytic genes, and (3) tricarboxylic acid cycle genes. Here we develop a procedure that calculates the sensitivity of optimal cellular growth to altered flux levels of these essential gene products. The results indicate that the E. coli metabolic network is robust with respect to the flux levels of these enzymes. The metabolic flux in the transketolase and the tricarboxylic acid cycle reactions can be reduced to 15% and 19%, respectively, of the optimal value without significantly influencing the optimal growth flux. The metabolic network also exhibited robustness with respect to the ribose-5-phosphate isomerase, and the ribose-5-phosephate isomerase flux was reduced to 28% of the optimal value without significantly effecting the optimal growth flux. The metabolic network exhibited limited robustness to the three-carbon glycolytic fluxes both increased and decreased. The development presented another dimension to the use of FBA to study the capabilities of metabolic networks.

Systems biology and the integration of mechanistic explanation and mathematical explanation

“…In some unicellular organisms whose genome has been sequenced and molecular functioning has been well-characterized, including the bacterium Escherichia coli and the eukaryotic yeast Saccharomyces cerevisiae, the robustness of metabolic networks has been mathematically modeled (Edwards & Palsson, 1999, 2000aSmart et al, 2008). Edwards and Palsson (2000b) show that the rate of two different enzymatic reactions in E. coli can be reduced to 15% and 19%, respectively, of the optimal rate, without significantly diminishing the system's overall metabolic flux (whereas upon further individual reaction rate reduction the system's flux drops rapidly). In contrast, for a third reaction, the threshold rate above which overall metabolic flux is largely unaffected is 70%.…”

Integrated Energy and Flux Balance Based Multiobjective Framework for Large-Scale Metabolic Networks

“…Most of the literature on constraints-based metabolic network optimality deals with unicellular organisms where the main objective is growth of biomass. 9,10 In mammalian systems, various phenotypes are encountered, some of which exhibit proliferation, and others expression of organ-specific or ''differentiated'' functions. Several objectives should be considered before making any conclusions about the optimal states of such systems.…”

“…4e and 6e), the changes in flux required to move from points I to J along the Pareto frontier significantly differed between gluconeogenic and glycolytic hepatocytes (Figs. 5e and 7e), mainly with respect to gluconeogenesis fluxes (2-6), and TCA cycle fluxes (8)(9)(10)(11)(12)(13). In the gluconeogenesis mode, TCA cycle fluxes are higher because of increased demand to produce ATP (gluconeogenesis consumes ATP too), since glycolysis itself produces ATP (2 molecules of ATP for 1 molecule of glucose consumed).…”

“…The quantification of intracellular metabolic fluxes is widely used for investigation of the metabolism in mioorganisms 9,10,[17][18][19][20][21] and mammalian systems. 1,[5][6][7][8][11][12][13][14]24,25 Flux balance analysis (FBA) uses stoichiometric and mass balance constraints to compute the intracellular fluxes.…”

Evolutionary Developmental Biology and the Limits of Philosophical Accounts of Mechanistic Explanation

“…In various unicellular organisms whose genome has been sequenced and molecular functioning has been wellcharacterized, including the bacterium Escherichia coli and the eukaryotic yeast Saccharomyces cerevisiae, the robustness of metabolic networks has been mathematically modeled Palsson 1999, 2000a;Smart et al 2008). Edwards and Palsson (2000b) show that the rate of two individual enzymatic reactions in E. coli can be reduced to 15% and 19%, respectively, of the optimal rate, without significantly diminishing the system's overall metabolic flux (if an individual reaction's rate goes below these values the system's flux drops rapidly). In contrast, for a third reaction, the threshold rate above which overall metabolic flux is largely unaffected is 70%.…”