Metabolic Control Analysis
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 F
1-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
