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Understanding the organization of reaction fluxes in cellular metabolism fromthe stoichiometry and the topology of the underlying biochemical network is acentral issue in systems biology. In this task, it is important to devisereasonable approximation schemes that rely on the stoichiometric data only,because full-scale kinetic approaches are computationally affordable only forsmall networks (e.g. red blood cells, about 50 reactions). Methods commonlyemployed are based on finding the stationary flux configurations that satisfymass-balance conditions for metabolites, often coupling them to localoptimization rules (e.g. maximization of biomass production) to reduce the sizeof the solution space to a single point. Such methods have been widely appliedand have proven able to reproduce experimental findings for relatively simpleorganisms in specific conditions. Here we define and study a constraint-basedmodel of cellular metabolism where neither mass balance nor flux stationarityare postulated, and where the relevant flux configurations optimize the globalgrowth of the system. In the case of E. coli, steady flux states are recoveredas solutions, though mass-balance conditions are violated for some metabolites,implying a non-zero net production of the latter. Such solutions furthermoreturn out to provide the correct statistics of fluxes for the bacterium E. coliin different environments and compare well with the available experimentalevidence on individual fluxes. Conserved metabolic pools play a key role indetermining growth rate and flux variability. Finally, we are able to connectphenomenological gene essentiality with `frozen' fluxes (i.e. fluxes withsmaller allowed variability) in E. coli metabolism.

Identifying essential genes in E. coli from a metabolic optimization principle