JWS Online

marinhernandez3

marinhernandez3

 Detail
 Download

Reactions

AK

AK

ATP + AMP = {2.0}ADP

ALDO

ALDO

FBP = DHAP + G3P

ATPases

ATPases

ATP > ADP + Pi

DHases

DHases

NADH = NAD

ENO

ENO

_2PG = PEP

GAPDH

GAPDH

NAD + G3P + Pi = _13BPG + NADH

GLUT

GLUT

Gluout = Gluin

Glycogen_degradation

Glycogen degradation

glycogen + Pi > G6P

Glycogen_synthesis

Glycogen synthesis

G6P + ATP > glycogen + ADP + {2.0}Pi

HK

HK

Gluin + ATP = G6P + ADP

HPI

HPI

G6P = F6P

LDH

LDH

NADH + Pyr = Lacin + NAD

MCT1

MCT1

Lacin = Lacout

MPM

MPM

Pyr + {13.0}ADP + {13.0}Pi > {13.0}ATP

OxPhos

OxPhos

ADP + Pi > ATP

PFK1

PFK1

F6P + ATP = FBP + ADP

PGAM

PGAM

_3PG = _2PG

PGK

PGK

_13BPG + ADP = _3PG + ATP

PPP

PPP

G6P > _6PG

PYK

PYK

PEP + ADP = Pyr + ATP

TK

TK

Xy5P + Ery4P > G3P + F6P

TPI

TPI

DHAP = G3P

Parameters

Global parameters
AK
ALDO
ATPases
DHases
ENO
GAPDH
GLUT
Glycogen_degradation
Glycogen_synthesis
HK
HPI
LDH
MCT1
MPM
OxPhos
PFK1
PGAM
PGK
PPP
PYK
TK
TPI

Fixed species

Initial values

Functions and Rules

Events

Constraints

Note that constraints are not enforced in simulations. It remains the responsibility of the user to verify that simulation results satisfy these constraints.


Species:

Reactions:

Middle-click: pin/unpin nodes
Shift-click: pool/unpool species
Right-click: context menu
Apply alternate model layout to overlapping elements in current model:

log scales

y-axis min/max

x-axis min/max

Modeling cancer glycolysis under hypoglycemia, and the role played by the differential expression of glycolytic isoforms.

  • Alvaro Marín-Hernández
  • Sayra Y López-Ramírez
  • Isis Del Mazo-Monsalvo
  • Juan C Gallardo-Pérez
  • Sara Rodríguez-Enríquez
  • Rafael Moreno-Sánchez
  • Emma Saavedra
FEBS J. 2014; 281 (15): 3325-3345
Abstract
UNLABELLED: The effect of hypoglycemia on the contents of glycolytic proteins, activities of enzymes/transporters and flux of HeLa and MCF-7 tumor cells was experimentally analyzed and modeled in silico. After 24 h hypoglycemia (2.5 mm initial glucose), significant increases in the protein levels of glucose transporters 1 and 3 (GLUT 1 and 3) (3.4 and 2.1-fold, respectively) and hexokinase I (HKI) (2.3-fold) were observed compared to the hyperglycemic standard cell culture condition (25 mm initial glucose). However, these changes did not bring about a significant increase in the total activities (Vmax ) of GLUT and HK; instead, the affinity of these proteins for glucose increased, which may explain the twofold increased glycolytic flux under hypoglycemia. Thus, an increase in more catalytically efficient isoforms for two of the main controlling steps was sufficient to induce increased flux. Further, a previous kinetic model of tumor glycolysis was updated by including the ratios of GLUT and HK isoforms, modified pyruvate kinase kinetics and an oxidative phosphorylation reaction. The updated model was robust in terms of simulating most of the metabolite levels and fluxes of the cells exposed to various glycemic conditions. Model simulations indicated that the main controlling steps were glycogen degradation > HK > hexosephosphate isomerase under hyper- and normoglycemia, and GLUT > HK > glycogen degradation under hypoglycemia. These predictions were experimentally evaluated: the glycolytic flux of hypoglycemic cells was more sensitive to cytochalasin B (a GLUT inhibitor) than that of hyperglycemic cells. The results indicated that cancer glycolysis should be inhibited at multiple controlling sites, regardless of external glucose levels, to effectively block the pathway.
DATABASE: The mathematical models described here have been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.mib.ac.uk/database/achcar/index.html. [Database section added 21 July 2014 after original online publication].
hyper-glycemic