Quantitative elementary mode analysis of metabolic pathways: the example of yeast glycolysis.
BACKGROUND
Elementary mode analysis of metabolic pathways has proven to be a valuable tool for assessing the properties and functions of biochemical systems. However, little comprehension of how individual elementary modes are used in real cellular states has been achieved so far. A quantitative measure of fluxes carried by individual elementary modes is of great help to identify dominant metabolic processes, and to understand how these processes are redistributed in biological cells in response to changes in environmental conditions, enzyme kinetics, or chemical concentrations.


RESULTS
Selecting a valid decomposition of a flux distribution onto a set of elementary modes is not straightforward, since there is usually an infinite number of possible such decompositions. We first show that two recently introduced decompositions are very closely related and assign the same fluxes to reversible elementary modes. Then, we show how such decompositions can be used in combination with kinetic modelling to assess the effects of changes in enzyme kinetics on the usage of individual metabolic routes, and to analyse the range of attainable states in a metabolic system. This approach is illustrated by the example of yeast glycolysis. Our results indicate that only a small subset of the space of stoichiometrically feasible steady states is actually reached by the glycolysis system, even when large variation intervals are allowed for all kinetic parameters of the model. Among eight possible elementary modes, the standard glycolytic route remains dominant in all cases, and only one other elementary mode is able to gain significant flux values in steady state.


CONCLUSIONS
These results indicate that a combination of structural and kinetic modelling significantly constrains the range of possible behaviours of a metabolic system. All elementary modes are not equal contributors to physiological cellular states, and this approach may open a direction toward a broader identification of physiologically relevant elementary modes among the very large number of stoichiometrically possible modes.
Schwartz
Jean-Marc
JM
Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 611-0011, Japan. jean@kuicr.kyoto-u.ac.jp
Kanehisa
Minoru
M
D000465:Algorithms; D000431:Ethanol / Q000378:metabolism; D005941:Glucokinase / Q000378:metabolism; D005947:Glucose / Q000378:metabolism*; D005956:Glucose-6-Phosphate Isomerase / Q000378:metabolism; D006019:Glycolysis; D007700:Kinetics; D008954:Models, Biological; D025221:Phosphofructokinases / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000201:enzymology / Q000378:metabolism*; D012984:Software
D000431:Ethanol; D025221:Phosphofructokinases; D005941:Glucokinase; D005956:Glucose-6-Phosphate Isomerase; D005947:Glucose