Metabolic control analysis provides a theoretical framework for analyzing the control and regulation of metabolism (Fell, 1997). At a practical level, the introduction of metabolic control analysis has had two important consequences for the empirical analysis of plant metabolism. First, by providing a new set of fundamental parameters for characterizing metabolic pathways, particularly flux control coefficients, elasticities, and response coefficients, metabolic control analysis has stimulated a substantial effort to measure these quantities in an attempt to put the description of the control and regulation of plant metabolism on a firm foundation (Stitt and Sonnewald, 1995). Inevitably, this has involved the characterization of many transgenic lines since genetic manipulation provides the most versatile way of altering the endogenous level of specific enzymes for the measurement of flux control coefficients; and as discussed in the following section, this rigorous approach has provided ample evidence for the delocalized control of flux and for the complexity of the regulatory interactions in plant metabolic networks. Second, as illustrated by the modeling of the Calvin cycle described in the previous section (Poolman et al., 2000), metabolic control analysis provides a tool for analyzing steady-state kinetic models and for deducing flux control coefficients. This indirect approach to the determination of flux control coefficients further emphasizes the way in which control is distributed throughout the network and the dependence of this distribution on the prevailing physiological state of the organism.
These practical applications of metabolic control analysis are complemented by the important theoretical conclusions that have emerged concerning the feasibility of flux manipulation or metabolic engineering. First, overexpression of a single enzyme in a pathway is likely to have only a limited impact on flux because even if the chosen enzyme has a significant flux control coefficient in the wild-type plant, control will be redistributed to other steps in the pathway as the level of the enzyme is increased. The validity of this conclusion, and its challenging message for the plant metabolic engineer, has been borne out by a large body of experimental evidence from genetically engineered plants, including the notable and early failure to increase glycolytic flux in potato tubers via the overexpression of phosphofructokinase (Burrell et al., 1994). Second, overexpressing multiple pathway enzymes may lead to an increased flux, as demonstrated for tryptophan synthesis in yeast (Niederberger et al., 1992). In effect, this strategy can be seen to mimic the coordinated upregulation of gene expression that occurs in many physiological responses, for example, in the mobilization of storage lipid during the germination of Arabidopsis thaliana (Rylott et al., 2001), but it poses the problem of how to produce a coordinated change in the expression of several genes in a transformed plant. Third, the success of any attempt to increase the flux through a pathway also depends on maintaining the supply of the necessary substrates and ensuring that there is an increased demand for the product. In support of this conclusion, recent investigations have shown that the starch content and yield of potato tubers can be increased by downregulating the plastidic isoform of adenylate kinase, apparently as a direct result of increasing the availability of plastidic ATP for ADPglucose synthesis (Regierer et al., 2002); and the glycolytic flux in E. coli has been enhanced by introducing a soluble F1-ATPase to provide a sink for ATP (Koebmann et al., 2002; Oliver, 2002). Both these investigations are notable for their manipulation of a coenzyme that is necessarily involved in multiple reactions, and establishing the extent to which the observed phenotypes can be attributed exclusively to the direct effect of changes in ATP level and turnover may be problematic. However, the success of these manipulations emphasizes just how widely control is distributed in metabolic networks and hence the difficulty in selecting targets for manipulation.
The relationship between the substrates, enzymes, and fluxes in complex metabolic networks revealed by metabolic control analysis emphasizes the intrinsic difficulty of rational metabolic engineering. Moreover, while it is possible to predict that some strategies are likely to be successful—for example, diverting a small proportion of a flux into a novel product or eliminating the formation of a toxic product (Morandini and Salamini, 2003)—there is no certainty in the outcome. Moreover, engineering objectives that require extensive redirection of the fluxes through the central pathways of metabolism are likely to be particularly challenging and may be too ambitious or even intrinsically impossible without wholesale restructuring of the network (Morandini and Salamini, 2003). Despite this assessment, the recent progress in engineering increased starch production in potato tubers (Regierer et al., 2002) highlights the importance of sustained empirical investigations that are guided by a rigorous understanding of metabolic control.
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