Increasing the flux in metabolic pathways: A metabolic control analysis perspective

Author(s):  
David A. Fell
2018 ◽  
Author(s):  
Ziwei Dai ◽  
Jason W. Locasale

AbstractNutrition and metabolism are fundamental to cellular function in physiological and pathological contexts. Metabolic activity (i.e. rates, flow, or most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis in several representative topological structures of metabolic pathways as models, we derive exact results and conduct computer simulations that define relationships between thermodynamics, enzyme activity, and flux control. We confirm that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, metabolic pathways have a more adaptable pattern of regulation, controlled minimally by thermodynamics and not necessarily by the specific enzyme that generates the given reaction. These findings show how the control of metabolic pathways, which are rarely very far from equilibrium, is largely set by the overall flux through a pathway rather than by the enzyme which generates the flux or by thermodynamics.


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.


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

2020 ◽  
Author(s):  
Sophia Tsouka ◽  
Meric Ataman ◽  
Tuure Hameri ◽  
Ljubisa Miskovic ◽  
Vassily Hatzimanikatis

AbstractThe advancements in genome editing techniques over the past years have rekindled interest in rational metabolic engineering strategies. While Metabolic Control Analysis (MCA) is a well-established method for quantifying the effects of metabolic engineering interventions on flows in metabolic networks and metabolic concentrations, it fails to account for the physiological limitations of the cellular environment and metabolic engineering design constraints. We report here a constraint-based framework based on MCA, Network Response Analysis (NRA), for the rational genetic strain design that incorporates biologically relevant constraints, as well as genome editing restrictions. The NRA core constraints being similar to the ones of Flux Balance Analysis, allow it to be used for a wide range of optimization criteria and with various physiological constraints. We show how the parametrization and introduction of biological constraints enhance the NRA formulation compared to the classical MCA approach, and we demonstrate its features and its ability to generate multiple alternative optimal strategies given several user-defined boundaries and objectives. In summary, NRA is a sophisticated alternative to classical MCA for rational metabolic engineering that accommodates the incorporation of physiological data at metabolic flux, metabolite concentration, and enzyme expression levels.


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