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Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment.
The first release of the COBRA Toolbox in 2007 provided access to a variety of methods, including flux balance analysis, gene essentiality analysis, and minimization of metabolic adjustment analysis (Table 1). Since the release of the first version of the COBRA Toolbox, many additional COBRA-related methods have been published44-48. In version 2. 0 of the COBRA Toolbox, we have extended the capabilities to include: geometric FBA44, Loop law49, creation of context-specific subnetwork models using omics data14, 25, Monte Carlo sampling15, 50-52, 13C fluxomics, gap filling45 , 53, metabolic engineering46-48, and visualization of computational models of metabolism (Table 1 / Figure 2).
For a number of bacterial species, the maximization of the growth rate (or yield as they are not always independent) was reported to be successful at predicting experimentally observed exponential growth phenotypes –. Accounting for possible minimization of cellular “effort” in utilizing available energy and external resources, it was found that the minimization of the sum of reaction fluxes (Manhattan norm of the flux vector) was a good way to predict the outcome of the metabolic network , which was interpreted as a maximally efficient use of the available biochemical reactions. Finally, for predicting the metabolism of gene-deletion mutants, a successful objective function was to find the reaction flux distribution profiles most similar to the ones of the wild-type growing in the same conditions, an objective function known as minimization of metabolic adjustments.
Among the above approaches, intracellular flux distribution can be calculated by maximization of cell biomass or minimization of metabolic adjustments. The calculated results which largely reveal the physiological state of the wild-type strain can be used to inactivate the target pathways for improved production. In addition to the knockout prediction, the overexpression prediction algorithm has also been recently developed to direct application of product overproduction [23,24].