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Principles of optimal metabolic network operation

Jens Nielsen

Author Affiliations

  • Jens Nielsen, 1 Center for Microbial Biotechnology, Technical University of Denmark, Lyngby, Denmark

What is the optimal operation of metabolic networks? This question is interesting to answer, as it would provide information on which underlying principles have shaped metabolic networks during evolution, and it may allow us to identify some simple rules governing the operation of metabolic networks under different growth conditions. Such rules might be important for metabolic network engineering—for example when designing better microbes for the production of fuels, chemicals and materials—but also for the identification of the regulatory mechanisms that ensure the different operations of metabolism.

Using reconstructed genome‐scale metabolic models, the group of Palsson has shown on several occasions that a well suited guiding principle—the so‐called ‘objective function’ in the terminology used in metabolic flux balance analysis—for operation of metabolic networks is optimization of growth (Price et al, 2004). In other words, through evolution, microorganisms have evolved in such a way that their metabolic networks ensure the most efficient conversion of carbon and energy to produce more cells. This principle seems to be robust as it was elegantly illustrated in a study where the group looked at growth of the bacterium Escherichia coli on glycerol. They found that the metabolic network did not initially operate according to the optimal growth principle. However, under selection pressure on glycerol, E. coli evolved to eventually maximize their growth rate (Ibarra et al, 2002). The use of this simple optimization criterion has wide applications, as it has allowed the use of genome‐scale metabolic models for simulation of different phenotypes in functional genomics studies (Edwards et al, 2001), for example, analysis of gene essentiality, as well as for identification of targets for metabolic engineering, such as for improving bioethanol production (Bro et al, 2006).

The study of Bacillus subtilis, in the group of Uwe Sauer (Fischer and Sauer, 2005) showed, however, that bacteria may not necessarily have evolved solely to optimize growth. By analyzing the metabolic fluxes in a large number of deletion mutants they found that some mutants grew faster than the wild‐type strain, showing that bacteria may have regulatory systems that ensure that the metabolic network does not operate solely according to maximized biomass production. Even though this kind of findings is likely to generalize only to bacteria and not to eukaryotes (in yeast there has not been identified a deletion mutant that would grow faster than the wild‐type), it clearly points to the fact that bacteria do not operate their metabolic network according to a single rule of optimization. In a recent paper published in Molecular Systems Biology (Schuetz et al, 2007), the group of Sauer sheds more light on the optimality principles for operation of metabolic networks in bacteria. In a thorough study of E. coli they used a metabolic model to evaluate different optimization criteria. In order to score the different optimization criteria they compare simulated fluxes through the different parts of the metabolic network with values obtained from experiments with 13C‐labelled glucose, a technique that has shown to give very robust estimation of metabolic fluxes (Nielsen, 2003). Based on that study, they found that when cells are growing under carbon (and energy) limited conditions the metabolic network does indeed operate according to the principle of growth optimization. However, when cells are growing under non‐limiting conditions, that is with an excess of carbon and energy, optimal growth gives a poor description of the operation of the metabolic network. Instead, it appears that, under these conditions, the objective of the network is to maximize ATP production per flux unit. In other words, for unlimited growth, the metabolic network operates to produce as much ATP from as few enzymatic reactions as possible, rather than to maximize overall ATP production.

Metabolic networks generally have a large number of degrees of freedom. As a consequence, using an optimization criterion such as maximal growth often does not result in a unique solution in terms of the metabolic fluxes, and this is one of the reasons why a single optimization criteria does not provide a correct description of the metabolic network under different growth conditions. The findings of Schuetz and co‐workers are very interesting in a biological context, as it tells us that under carbon and energy limitation bacteria aim at optimizing the use of the scarce resources to maximize their growth, probably as a way to out‐compete other microbes simply by growing as fast as possible. On the other hand if there is an excess of carbon and energy available, then the cells typically adopt an over‐flow metabolism (production of acetate in the case of E. coli) resulting in less efficient energy utilization of the carbon and energy source. The findings reported by Sauer and co‐workers indicate that under these conditions the metabolic network will seek to operate in such a way that as few enzymatic steps as possible are being used to generate the required ATP. There can be different reasons for why cells have an over‐flow metabolism under conditions of carbon and energy excess, and hence why they change the optimization strategy of their metabolic network. Schuetz and co‐workers give different possible explanations, with the most likely being that under carbon and energy excess the cells operate with suboptimal ATP yields, as this will allow dissipation of more energy and thereby enable higher catabolic rates. This argument is in line with theories from non‐equilibrium thermodynamics, which state that the flux of a reaction is directly correlated with the difference between the Gibbs free energy of products and substrates. The study of Schuetz and co‐workers has therefore clearly provided new insights into the guiding principles behind operation of metabolic networks, and will allow for a more widely use of metabolic models for simulation of microbial growth.

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