Systems Biology Approaches to Cancer Energy Metabolism - Systems Biology of Metabolic and Signaling Networks - page 213

(Mar ´ n-Hern ´ ndez et al. 2006 ). To circumvent these problems, we applied kinetic
modeling combined with MCA to determine the control coefficients of tumor
glycolysis.
9.5 Kinetic Modeling of Glycolysis in Tumor Cells
Kinetic modeling is a bottom-up systems biology approach that constructs detailed
computational descriptions of metabolic/signal transduction pathways in order to
understand how new properties emerge when the pathway components interact with
each other (Westerhoff 2011 ). In kinetic models of metabolic pathways, each
reaction is defined by a rate equation that includes the kinetic properties of the
individual enzymes/transporters, namely, maximal velocity and the affinity
constants for all the ligands. Kinetic models that also perform MCA have the
advantage of enabling to understand the control and regulation mechanisms of a
pathway which are not possible to elucidate from the kinetic properties of the
individual enzymes (Moreno-S´nchez et al. 2008 ; van Gend and Snoep 2008 ;
Cortassa et al. 2009 ). Recently, we built kinetic models of glycolysis in HeLa
(human cervico-uterine) and AS-30D (rat ascitic hepatoma) tumor cell lines to
identify the main controlling reactions in cells exposed to different environmental
conditions (Mar ´ n-Hern ´ ndez et al. 2011 ).
To build the models it was necessary to experimentally determine in cell-free
extracts under physiological conditions of pH, temperature (pH 7.0 and 37 C), and
ion composition (high K + ), several kinetic parameters, and variables: (a) the affinity
constants for ligands (substrates, products, activators, and inhibitors) of all
enzymes; (b) the maximal enzyme activity ( V max ) for the forward and reverse
reactions within cells; (c) the intracellular intermediary metabolite concentrations;
and (d) the glycolytic flux including its branches to glycogen synthesis/degradation,
pentose phosphate pathway, and mitochondrial pyruvate oxidation. These
determinations were performed under a defined metabolic steady state. The rate
equations for each reaction were obtained from the literature in which parameters
(a) and (b) were used to assign values to their constants. These equations and the
initial concentration of substrates were assembled to construct the kinetic model
using the metabolic simulators GEPASI v 3.3 (Mendes 1993 ) and COPASI (COm-
plex PAthway SImulator; Hoops et al. 2006 ). Comparisons of model simulations of
metabolite concentrations and fluxes with the in vivo determinations (variables ( c )
and ( d ), respectively) were used to validate the kinetic model and predict the in vivo
pathway behavior in two tumor cell lines.
For model refinement, a dynamic interplay between modeling and experimenta-
tion was carried out: model simulations helped to pinpoint what parameters/
variables had to be experimentally reevaluated which were then fed to the model
until simulation results converged with the in vivo pathway behavior. The three
most important modifications performed in the model were: (1) the inhibition of
hexose-6-phosphate isomerase (HPI) by some metabolites of glycolysis and