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was identified to be the most promising target, followed by aldolase, glycerol-3-
phosphate dehydrogenase, and glyceraldehyde-phosphate dehydrogenase.
Eisenthal and Cornish-Bowden (
1998
) have suggested that metabolite would accu-
mulate sufficiently to interfere with the action of competitive inhibitors. Following
this reasoning, competitive inhibition of pyruvate export was found more adequate
in their studies. The most promising target the glucose transporter (Bakker
et al.
1999
) should however be insensitive to this argument, as the extracellular
glucose concentration will not be affected by such an inhibitor. Also glycolytic and
energy metabolism in cancer has been studied from the control analysis perspective,
leading to new drug targets (Moreno-Sanchez et al.
2010
).
Letellier et al. (
1998
) detailed an important application of MCA in medicine,
i.e. to enzyme deficiencies, in mitochondrial myopathies. MCA explained why flux
is not considerably reduced above a certain threshold activity of the enzyme, and
only after further inhibition, the flux decreased drastically. In case of enzyme
deficiencies, control coefficients can only be used as a guideline. Therefore,
Schuster and Holzhutter (
1995
) have used kinetic modelling to address the impact
of enzyme deficiencies in erythrocytes.
One of the limitations with current chemotherapies is not that they fail to kill
cancer cells but that they fail to destroy it at doses which are not harmful to normal
cells. The therapeutic window between cytotoxicity in malignant cells versus
normal tissue is too narrow. A key to finding targets where cancer may be differen-
tially sensitive is to try and understand what are the differences in flux control
between cancer and normal cells. Then drugs may be targeted to steps where the
flux control is much higher in the tumour than in healthy tissues. These steps could
be enzymes (Moreno-Sanchez et al.
2010
) as well as signal transduction proteins
(Hornberg et al.
2005a
).
3.11 Software for Control Analysis
Several programmes are adopting the MCA approach and thus we avail of some
useful tools for various aspects of MCA. Metabolic simulators such as COPASI
(Hoops et al.
2006
) are programmes for simulation of steady states and transient
behaviour of biochemical pathways (including several compartments). They pro-
vide features to enable calculation of all the enzyme elasticities and flux and
concentration control coefficients. JWS online (Olivier and Snoep
2004
) is a public
model repository enabling the use of realistic models through a web interface.
Control coefficients become available at the clicking of a button and their depen-
dence on network properties can be computed in close to realistic models, including
those of signal transduction.
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