scholarly journals Glucose and the ATP paradox in yeast

2000 ◽  
Vol 352 (2) ◽  
pp. 593-599 ◽  
Author(s):  
Oscar J. G. SOMSEN ◽  
Martijn A. HOEBEN ◽  
Eugenia ESGALHADO ◽  
Jacky L. SNOEP ◽  
Diana VISSER ◽  
...  

A sustained decrease in the intracellular ATP concentration has been observed when extra glucose was added to yeast cells growing aerobically under glucose limitation. Because glucose degradation is the main source of ATP-derived free energy, this is a counter-intuitive phenomenon, which cannot be attributed to transient ATP consumption in the initial steps of glycolysis. We present a core model for aerobic growth in which glucose supplies carbon, as well as free energy, for biosynthesis. With Metabolic Control Analysis and numerical simulations, we demonstrate that the decrease in the ATP concentration can be reproduced if the biosynthetic route is more strongly activated by carbon substrates than is the catabolic (ATP-producing) route.

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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