The Control Analysis of Signal Transduction - Systems Biology of Metabolic and Signaling Networks - page 53

whereas on a second-to-minute timescale most signal flux into and out of GS turned
to glutamate and the adenylylation state. On a minute timescale it was the
adenylylation state signal flux into GS that dominated. On average for the whole
transient period, some 60 % of the regulation was mediated by signal through the
adenylylation state of GS and some 40 % by ADP, glutamate, glutamine, and ATP
(Bruggeman et al. 2005 ). Again, i.e. also when it comes down to GS regulation, there
is no single regulatory route, but all of the signal transducing routes contribute to
different degrees on different timescales.

3.10 Medical Implications of Control Analysis

In medical applications the focus of control analysis applications has been to
identify:

1. The enzymes having high flux control coefficients in the pathway influenced by
disease; these are likely to be potent drug targets reducing metabolic flux
through the pathway upon their inhibition.
2. The enzymes having high flux control coefficients in the pathogenic microor-
ganism, for which the enzymes catalysing the homologous reaction in the
complementary human metabolic pathway have a low flux control coefficient;
these can be targeted to suppress the growth of microorganism with minimal
effect on the humans.
3. Bottlenecks of processes important for nutrition; Rigoulet et al. ( 1988 ) calcu-
lated the redistribution of flux control coefficients in the TCA cycle in brain
edema. In diabetes it is important to know which enzymes control the carbon
flux into the gluconeogenic pathway. Groen et al. ( 1983 ) and Rigoulet
et al. ( 1987 ) have shown that pyruvate carboxylase exerts much flux control
and would, therefore, be a potent drug target.

MCA has been used to rank drug targets to suppress Trypanosoma brucei , the
parasite causing sleeping disease. This was done by calculating which steps in
glycolysis needed the least inhibition to achieve a certain inhibition of the glyco-
lytic flux (Bakker et al. 1999 ). The glucose transporter in the Trypanosoma brucei

Fig. 3.6 (continued) cytoplasm to activate the transcription of GAL genes. K i ( i 1-4) and K d

values represent dissociation constants for various protein-protein and DNA-protein interactions,
respectively. All the parameters values, genes, and protein concentrations were taken from (Verma
et al. 2003 ). (b) Simulated fractional protein expression from GAL genes having one and two
binding sites for Gal4p dimer in response to galactose signalling. (c) Schematic of complete
domino model for galactose metabolism in Saccharomyces cerevisiae , adopted from domino
glucose metabolism (Verma et al. 2013 ). (d) Simulated profile of flux control coefficients for
fractional saturation of galactose permease and galactokinase, enzymes catalysing reaction 1 and
reaction 2, respectively, of domino galactose metabolism. Model parameters were replicated from
domino glucose metabolism (Verma et al. 2013 )

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