The Control Analysis of Signal Transduction - Systems Biology of Metabolic and Signaling Networks - page 39

from, and began to explore the properties of metabolic pathways composed of
enzymes and transporters exhibiting the known nonlinear kinetics with respect to
the amount of substrates, products, and effectors (Michaelis and Menten 1913 ) that
a paradigm shift towards systems biology occurred. With the limited computing
power then available, many still approached this through computer simulations. Jim
Burns and Henrik Kacser (Kacser and Burn 1973 ) were one team, performing
numerical simulations on an analogue computer examining what one should expect
for the dependence of flux on gene dosage. Reinhart Heinrich and Tom Rapoport
(Heinrich and Rapoport 1974 ) were another team, examining how the flux through a
model of the erythrocyte's glycolytic pathway would depend on the activities of its
enzymes. Their computations led to the realisation that in biochemical pathways,
because of the presence of back pressure effects of substances downstream on
upstream reactions, there was no a priori reason why all control of pathway flux
should reside in one enzyme, or why one should expect one gene to be dominant in
terms of controlling the pathway flux. Rather one should expect recessiveness for
all enzyme genes (Kacser and Burn 1973 ). This led to metabolic control analysis
(MCA), a pendant of biochemical systems theory (BST) (Savageau 1976 ).
Metabolic control analysis has played at least five important roles in biology and
biochemistry, roles that have later become characteristic of systems biology: it has
demonstrated the importance of (1) good definitions (Kholodenko et al. 1995 ),
(2) quantitative approaches, (3) consensus or standardisation (Burns et al. 1985 ),
(4) integration of experimental and theoretical work, and (5) principles emerging
from molecular networking (Westerhoff et al. 2009a , b ). MCA tells us that qualita-
tive and unfounded statements such as the ones reviewed above in the context of
defining metabolic control have no meaning in biology and thereby only add to
confusion: flux control may well be distributed over all the steps in a pathway and
has been shown to be distributed in almost all biological cases studied, with some
steps having a higher degree of flux control than others.
Good definitions have more than one property, i.e. they should be unequivocal,
operational, and understandable also to the nonexperts. In view of the latter, the
magnitude of the flux control coefficients of a pathway step can be seen as a
percentage of control exerted by that step over the flux of interest. It is also the
percentage change in steady-state flux caused by a 1 % activation of only that step.
In practice, pathway control is shared between all enzymes of the network (i.e. not
only of the pathway), in proportions that differ between pathways. The attractive
and thereby rather persistent, but flawed, concept of “the rate-limiting” step in a
network process has been invalidated experimentally for metabolic (Groen
et al. 1982 ) and gene expression (Jensen et al. 2000 ) pathways. For signal transduc-
tion pathways the subtleties in control are not less (Heinrich et al. 2002 ; Hornberg
et al. 2005a ).
Contrary to what is sometimes proposed as MCA is not a mere sensitivity analysis
of fluxes and metabolite concentrations. Where a sensitivity analysis treats the
sensitivities of a flux with respect to all parameters (e.g. temperature, enzyme activity,
or Michaelis-Menten constant) equally, MCA starts from the complete set of
sensitivities of a flux [or other state function (Westerhoff and Dam 1987 )] for all