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two environmental variables onto a two-dimensional plane and identify phases
with distinct metabolic pathway utilization patterns. Some applications of PhPP
analysis include the study of optimal growth rates, adaptability of
microorganisms, metabolic network functions and capacities, and the impact of
gene regulations. Thus, PhPP analysis provides a way to guide experiments and
analyze phenotypic functions based on genome-scale metabolic networks.
Regulatory on/off minimization
(ROOM) is a constraint-based algorithm for
predicting the metabolic steady state fluxes after gene knockout. This method is
able to correctly identify short alternative pathways used for rerouting metabolic
fluxes in response to gene deletions.
8.3.5.
Metabolic control analysis
Metabolic control analysis
(MCA) is a quantitative and qualitative method for
studying how steady state properties of the network are interrelated with those of
individual reactions method for analyzing how the control of fluxes and
intermediate concentrations in a metabolic pathway is distributed among the
different enzymes that constitute the pathway.
One of the most important applications of MCA is in biotechnological
production processes, since it is important to reveal which enzyme(s) should be
activated in order to increase the synthesis rate of some given metabolite.
The relationship between stationary flux distribution and kinetic parameters is
highly nonlinear and to date, no general method to predict the effects of large
parameter variations is known or proved to be effective. For this reason, it is
convenient to consider small variations, i.e. assuming a linear relationship, so
that precise mathematical expression can be derived and metabolic network
behaviour quantitatively predicted.
In MCA, one studies the relative control exerted by each step (enzyme) on the
system's variables (fluxes and metabolite concentrations). This control is
measured by applying a perturbation to the step being studied and measuring the
effect on the variable of interest after the system has settled to a new steady state.
The basic mathematical tool is the sensitivity coefficient defined, for a generic
quantity
x
depending on a parameter
p
, as follows:
p
∆
x
p
∂
x
∂
ln
x
x
p
c
=
=
=
x
∆
p
x
∂
p
∂
ln
p
∆
x
→
0
p
∆
x
p
∂
x
∂
ln
x
x
p
c
=
=
=
x
∆
p
x
∂
p
∂
ln
p
∆
x
→
0
