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9.4 Metabolic Control Analysis of Tumor Glycolysis
Metabolic Control Analysis (MCA) is a Systems Biology approach that analyzes
metabolic networks with the goal of elucidating their underlying control and
regulation mechanisms (reviewed by Fell 1997 ; Moreno-S´nchez et al. 2008 ,
2010 ; Westerhoff 2008 ). On this regard, MCA makes a clear distinction between
control and regulation of metabolism. “Control” indicates the extent to which a
flux through a pathway, or the concentration of an intermediary metabolite, is
altered by changing the activity of one step, or group of steps, and is quantitatively
represented by flux- and concentration- control coefficients . “Regulation” refers to
how the flux of a pathway or a metabolite level is modified through the effect on the
rate of an individual step by cellular factors, including metabolites different to
substrates/products of that step, enzyme activity modulators, and ions, and is
quantitatively represented by the response coefficient (Fell 1997 ). Initially used
for metabolic pathways at steady state, the MCA principles have been recently
applied to the analysis of time-dependent events and oscillations of metabolic and
signal transduction pathways (Westerhoff 2008 ). MCA has also been extended to
the regulation of cellular processes by gene expression and termed Hierarchical
Control Analysis (Westerhoff 2008 ).
MCA studies have demonstrated that the flux control of a particular metabolic
pathway is distributed, i.e., it is shared to different extents by all the participating
steps; thus, the existence of a unique rate-limiting step can be ruled out. MCA
allows to quantifying the degree of control exerted by each enzyme/transporter on
the pathway flux (flux control coefficient; FCC or C ai ) and on the metabolite
concentrations (concentration control coefficient; CCC or C X
ai ) where J is flux,
X is the concentration of a pathway intermediary, and ai is the activity a in the cells
of the pathway enzyme i . By applying this strategy using both “wet” and in silico
experimentation, the main controlling steps can be identified, thereby becoming
targets with the highest therapeutic potential.
Elasticity analysis, an in vivo experimental approach utilized in MCA to deter-
mine the control coefficients by groups of enzymes, was applied to glycolysis in
AS-30D rat tumor cells (Mar´n-Hern´ndez et al. 2006 ). The results indicated that
the pathway flux is mainly controlled (71 %) by the glucose transporter (GLUT)
and/or HK; PFK-1 controlled only by 6 %, whereas the rest of the control (25 %)
resided in the enzymes from aldolase (ALDO) to lactate dehydrogenase (LDH).
The low flux control exerted by PFK-1 is in agreement with the different control
distribution exhibited by glycolysis in normal versus tumor cells. These findings
also suggest that PFK-1 inhibition will probably be more harmful to normal than to
tumor cells.
However, a limitation of the flux-control coefficients determined from elasticity
coefficients is that their estimation requires reliable measurements of relatively
small gradual changes in the pathway metabolites. In addition, this strategy did not
allow us to elucidate the GLUT and HK flux control coefficients separately, neither
to calculate the individual control coefficients from the downstream glycolytic steps
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