scholarly journals Metabolic control analysis of biochemical pathways based on a thermokinetic description of reaction rates

1997 ◽  
Vol 321 (1) ◽  
pp. 133-138 ◽  
Author(s):  
Jens NIELSEN

Metabolic control analysis is a powerful technique for the evaluation of flux control within biochemical pathways. Its foundation is the elasticity coefficients and the flux control coefficients (FCCs). On the basis of a thermokinetic description of reaction rates it is here shown that the elasticity coefficients can be calculated directly from the pool levels of metabolites at steady state. The only requirement is that one thermodynamic parameter be known, namely the reaction affinity at the intercept of the tangent in the inflection point of the curve of reaction rate against reaction affinity. This parameter can often be determined from experiments in vitro. The methodology is applicable only to the analysis of simple two-step pathways, but in many cases larger pathways can be lumped into two overall conversions. In cases where this cannot be done it is necessary to apply an extension of the thermokinetic description of reaction rates to include the influence of effectors. Here the reaction rate is written as a linear function of the logarithm of the metabolite concentrations. With this type of rate function it is shown that the approach of Delgado and Liao [Biochem. J. (1992) 282, 919–927] can be much more widely applied, although it was originally based on linearized kinetics. The methodology of determining elasticity coefficients directly from pool levels is illustrated with an analysis of the first two steps of the biosynthetic pathway of penicillin. The results compare well with previous findings based on a kinetic analysis.

FEBS Letters ◽  
2002 ◽  
Vol 517 (1-3) ◽  
pp. 245-250 ◽  
Author(s):  
Achim M. Vogt ◽  
Holger Nef ◽  
Jutta Schaper ◽  
Mark Poolman ◽  
David A. Fell ◽  
...  

1997 ◽  
Vol 322 (1) ◽  
pp. 119-127 ◽  
Author(s):  
Simon THOMAS ◽  
Peter J. F. MOONEY ◽  
Michael M. BURRELL ◽  
David A. FELL

We have applied Metabolic Control Analysis (MCA) in an attempt to determine the distribution of glycolytic flux control between the steps of glycolysis in aged disks of potato tuber under aerobic conditions, using concentrations of glycolytic metabolites in tuber tissue from a range of transgenic potato plants and published enzyme kinetic data. We modelled the substrate and effector kinetics of potato tuber phosphofructokinase (PFK) by reanalysing published results. Despite the scarcity of reliable kinetic data, our results are in agreement with experimental findings namely that, under the conditions described, PFK has little control over glycolytic flux. Furthermore our analysis predicts that under these conditions far more control lies in the dephosphorylation of phosphoenolpyruvate and/or in the steps beyond. We have validated the results of our analysis in two ways. First, predictions based on calculated concentration control coefficients from the analysis show generally good agreement with observed metabolite deviation indices discussed in the preceding paper [Thomas, Mooney, Burrell, and Fell (1997) Biochem. J.322, 111Ő117]. Second, sensitivity analysis of our results shows that the calculated control coefficients are robust to errors in the elasticities used in the analysis, of which relatively few need to be known accurately. Experimental and control analysis results agree with previous predictions of MCA that strong co-operative feedback inhibition of enzymes serves to move flux control downstream of the inhibiting metabolite. We conclude that MCA can successfully model the outcome of experiments in the genetic manipulation of enzyme amounts.


2018 ◽  
Author(s):  
David Andrew Fell

Metabolic Control Analysis defines the relationships between the change in activity of an enzyme and the resulting impacts on metabolic fluxes and metabolite concentrations at steady state. In many biotechnological applications of metabolic engineering, however, the goal is to alter the product yield. In this case, although metabolism may be at a pseudo-steady state, the amount of biomass catalysing the metabolism can be growing exponentially. Here, expressions are derived that relate the change in activity of an enzyme and its flux control coefficient to the change in yield from an exponentially growing system. Conversely, the expressions allow estimation of an enzyme's flux control coefficient over the pathway generating the product from measurements of the changes in enzyme activity and yield.


2019 ◽  
Vol 26 (36) ◽  
pp. 6652-6671 ◽  
Author(s):  
Emma Saavedra ◽  
Zabdi González-Chávez ◽  
Rafael Moreno-Sánchez ◽  
Paul A.M. Michels

In the search for therapeutic targets in the intermediary metabolism of trypanosomatids the gene essentiality criterion as determined by using knock-out and knock-down genetic strategies is commonly applied. As most of the evaluated enzymes/transporters have turned out to be essential for parasite survival, additional criteria and approaches are clearly required for suitable drug target prioritization. The fundamentals of Metabolic Control Analysis (MCA; an approach in the study of control and regulation of metabolism) and kinetic modeling of metabolic pathways (a bottom-up systems biology approach) allow quantification of the degree of control that each enzyme exerts on the pathway flux (flux control coefficient) and metabolic intermediate concentrations (concentration control coefficient). MCA studies have demonstrated that metabolic pathways usually have two or three enzymes with the highest control of flux; their inhibition has more negative effects on the pathway function than inhibition of enzymes exerting low flux control. Therefore, the enzymes with the highest pathway control are the most convenient targets for therapeutic intervention. In this review, the fundamentals of MCA as well as experimental strategies to determine the flux control coefficients and metabolic modeling are analyzed. MCA and kinetic modeling have been applied to trypanothione metabolism in Trypanosoma cruzi and the model predictions subsequently validated in vivo. The results showed that three out of ten enzyme reactions analyzed in the T. cruzi anti-oxidant metabolism were the most controlling enzymes. Hence, MCA and metabolic modeling allow a further step in target prioritization for drug development against trypanosomatids and other parasites.


2018 ◽  
Vol 46 (3) ◽  
pp. 555-564 ◽  
Author(s):  
Charles Affourtit ◽  
Ben Alberts ◽  
Jonathan Barlow ◽  
Jane E. Carré ◽  
Anthony G. Wynne

The canonical model of glucose-stimulated insulin secretion (GSIS) by pancreatic β-cells predicts a glucose-induced rise in the cytosolic ATP/ADP ratio. Such bioenergetic sensitivity to metabolic fuel is unusual as it implies that ATP flux is governed, to a significant extent, by ATP supply, while it is predominantly demand-driven in other cell types. Metabolic control is generally shared between different processes, but potential control of ATP consumption over β-cell bioenergetics has been largely ignored to date. The present paper offers a brief overview of experimental evidence that demonstrates ATP flux control by glucose-fuelled oxidative phosphorylation. Based on old and new data, it is argued that ATP supply does not hold exclusive control over ATP flux, but shares it with ATP demand, and that the distribution of control is flexible. Quantification of the bioenergetic control distribution will be important from basic and clinical perspectives, but precise measurement of the cytosolic ATP/ADP ratio is complicated by adenine nucleotide compartmentalisation. Metabolic control analysis of β-cell bioenergetics will likely clarify the mechanisms by which glucose and fatty acids amplify and potentiate GSIS, respectively. Moreover, such analysis may offer hints as to how ATP flux control shifts from ATP supply to ATP demand during the development of type 2 diabetes, and why prolonged sulfonylurea treatment causes β-cell deterioration.


Author(s):  
Sophia Tsouka ◽  
Meric Ataman ◽  
Tuure Hameri ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis

1993 ◽  
Vol 9 (3) ◽  
pp. 221-233 ◽  
Author(s):  
James C. Liao ◽  
Javier Delgado

2014 ◽  
Vol 86 (9) ◽  
pp. 1403-1403
Author(s):  
D. Volke ◽  
B. Engels ◽  
L. Wright ◽  
J. Gershenzon ◽  
S. Jennewein

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