public model
Model Identifier
Short description




SBML level 2 code generated for the JWS Online project by Jacky Snoep using PySCeS
Run this model online at http://jjj.biochem.sun.ac.za
To cite JWS Online please refer to: Olivier, B.G. and Snoep, J.L. (2004) Web-based modelling using JWS Online, Bioinformatics, 20:2143-2144







Biomodels Curation The model simulates the flux values as given for "kinetic model" in Table 1 of the paper. The model was successfully tested on Jarnac.

Related Publication
  • The principle of flux minimization and its application to estimate stationary fluxes in metabolic networks.
  • Holzhütter HG
  • European journal of biochemistry , 7/ 2004 , Volume 271 , pages: 2905-2922 , PubMed ID: 15233787
  • Humboldt-University Berlin, Medical School (Charité), Institute of Biochemistry, Berlin, Germany. hermann-georg.holzhuetter@charite.de
  • Cellular functions are ultimately linked to metabolic fluxes brought about by thousands of chemical reactions and transport processes. The synthesis of the underlying enzymes and membrane transporters causes the cell a certain 'effort' of energy and external resources. Considering that those cells should have had a selection advantage during natural evolution that enabled them to fulfil vital functions (such as growth, defence against toxic compounds, repair of DNA alterations, etc.) with minimal effort, one may postulate the principle of flux minimization, as follows: given the available external substrates and given a set of functionally important 'target' fluxes required to accomplish a specific pattern of cellular functions, the stationary metabolic fluxes have to become a minimum. To convert this principle into a mathematical method enabling the prediction of stationary metabolic fluxes, the total flux in the network is measured by a weighted linear combination of all individual fluxes whereby the thermodynamic equilibrium constants are used as weighting factors, i.e. the more the thermodynamic equilibrium lies on the right-hand side of the reaction, the larger the weighting factor for the backward reaction. A linear programming technique is applied to minimize the total flux at fixed values of the target fluxes and under the constraint of flux balance (= steady-state conditions) with respect to all metabolites. The theoretical concept is applied to two metabolic schemes: the energy and redox metabolism of erythrocytes, and the central metabolism of Methylobacterium extorquens AM1. The flux rates predicted by the flux-minimization method exhibit significant correlations with flux rates obtained by either kinetic modelling or direct experimental determination. Larger deviations occur for segments of the network composed of redundant branches where the flux-minimization method always attributes the total flux to the thermodynamically most favourable branch. Nevertheless, compared with existing methods of structural modelling, the principle of flux minimization appears to be a promising theoretical approach to assess stationary flux rates in metabolic systems in cases where a detailed kinetic model is not yet available.
Submitter of the first revision: Nicolas Le Novère
Submitter of this revision: Nicolas Le Novère
Modellers: Nicolas Le Novère

Metadata information

BioModels Database MODEL6624180840
BioModels Database BIOMD0000000070
PubMed 15233787
Taxonomy Homo sapiens

Curation status

Original model(s)


Connected external resources

SBGN view in Newt Editor

Name Description Size Actions

Model files

BIOMD0000000070_url.xml SBML L2V1 representation of Holzhutter2004_Erythrocyte_Metabolism 161.83 KB Preview | Download

Additional files

BIOMD0000000070-biopax2.owl Auto-generated BioPAX (Level 2) 110.99 KB Preview | Download
BIOMD0000000070-biopax3.owl Auto-generated BioPAX (Level 3) 159.50 KB Preview | Download
BIOMD0000000070.m Auto-generated Octave file 34.28 KB Preview | Download
BIOMD0000000070.pdf Auto-generated PDF file 375.65 KB Preview | Download
BIOMD0000000070.png Auto-generated Reaction graph (PNG) 814.82 KB Preview | Download
BIOMD0000000070.sci Auto-generated Scilab file 190.00 Bytes Preview | Download
BIOMD0000000070.svg Auto-generated Reaction graph (SVG) 97.85 KB Preview | Download
BIOMD0000000070.vcml Auto-generated VCML file 177.70 KB Preview | Download
BIOMD0000000070.xpp Auto-generated XPP file 25.14 KB Preview | Download
BIOMD0000000070_urn.xml Auto-generated SBML file with URNs 152.54 KB Preview | Download

  • Model originally submitted by : Nicolas Le Novère
  • Submitted: Sep 22, 2006 7:01:01 PM
  • Last Modified: Apr 8, 2016 4:29:23 PM
  • Version: 2 public model Download this version
    • Submitted on: Apr 8, 2016 4:29:23 PM
    • Submitted by: Nicolas Le Novère
    • With comment: Current version of Holzhutter2004_Erythrocyte_Metabolism
  • Version: 1 public model Download this version
    • Submitted on: Sep 22, 2006 7:01:01 PM
    • Submitted by: Nicolas Le Novère
    • With comment: Original import of holzhutter

(*) You might be seeing discontinuous revisions as only public revisions are displayed here. Any private revisions unpublished model revision of this model will only be shown to the submitter and their collaborators.

: Variable used inside SBML models

Reactions Rate Parameters
MgATP + Rib5P => MgAMP + PRPP compartment*Vmaxv25*(Rib5P*MgATP-PRPP*MgAMP/Keqv25)/((KATPv25+MgATP)*(KR5Pv25+Rib5P)) KR5Pv25=0.57 mM; Keqv25=100000.0 dimensionless; Vmaxv25=1.1 mM_per_hour; KATPv25=0.03 mM
MgATP + AMPf => ADPf + MgADP compartment*Vmaxv16/(KATPv16*KAMPv16)*(MgATP*AMPf-MgADP*ADPf/Keqv16)/((1+MgATP/KATPv16)*(1+AMPf/KAMPv16)+(MgADP+ADPf)/KADPv16+MgADP*ADPf/KADPv16^2) KAMPv16=0.08 mM; KADPv16=0.11 mM; KATPv16=0.09 mM; Keqv16=0.25 dimensionless; Vmaxv16=1380.0 mM_per_hour
GraP + Phi + NAD => NADH + Gri13P2 compartment*Vmaxv6/(KNADv6*KGraPv6*KPv6)*(NAD*GraP*Phi-Gri13P2*NADH/Keqv6)/(((1+NAD/KNADv6)*(1+GraP/KGraPv6)*(1+Phi/KPv6)+(1+NADH/KNADHv6)*(1+Gri13P2/K13P2Gv6))-1) K13P2Gv6=0.0035 mM; Keqv6=1.92E-4 dimensionless; KNADHv6=0.0083 mM; KGraPv6=0.005 mM; Vmaxv6=4300.0 mM_per_hour; KNADv6=0.05 mM; KPv6=3.9 mM
Xul5P + Rib5P => GraP + Sed7P compartment*Vmaxv23*(Rib5P*Xul5P-GraP*Sed7P/Keqv23)/((K1v23+Rib5P)*Xul5P+(K2v23+K6v23*Sed7P)*Rib5P+(K3v23+K5v23*Sed7P)*GraP+K4v23*Sed7P+K7v23*Xul5P*GraP) K6v23=0.00774 dimensionless; K7v23=48.8 dimensionless; K4v23=0.00496 mM; K1v23=0.4177 mM; K5v23=0.41139 dimensionless; Vmaxv23=23.5 mM_per_hour; Keqv23=1.05 dimensionless; K2v23=0.3055 mM; K3v23=12.432 mM
MgAMP => Mgf + AMPf compartment*EqMult*(MgAMP-Mgf*AMPf/KdAMP) KdAMP=16.64 mM; EqMult=1.0E7 hour_inverse
Fru16P2 => GraP + DHAP compartment*Vmaxv4/KFru16P2v4*(Fru16P2-GraP*DHAP/Keqv4)/(1+Fru16P2/KFru16P2v4+GraP/KiGraPv4+DHAP*(GraP+KGraPv4)/(KDHAPv4*KiGraPv4)+Fru16P2*GraP/(KFru16P2v4*KiiGraPv4)) Vmaxv4=98.91000366 mM_per_hour; KiGraPv4=0.0572 mM; KiiGraPv4=0.176 mM; KGraPv4=0.1906 mM; KFru16P2v4=0.0071 mM; Keqv4=0.114 mM; KDHAPv4=0.0364 mM
NADH + Pyr => Lac + NAD compartment*Vmaxv13*(Pyr*NADH-Lac*NAD/Keqv13) Vmaxv13=2800000.0 per_mM_hour; Keqv13=9090.0 dimensionless
Gri3P => Gri2P compartment*Vmaxv10*(Gri3P-Gri2P/Keqv10)/(Gri3P+K3PGv10*(1+Gri2P/K2PGv10)) K2PGv10=1.0 mM; Keqv10=0.145 dimensionless; K3PGv10=5.0 mM; Vmaxv10=2000.0 mM_per_hour
PEP + MgADP => MgATP + Pyr; ATPf, Fru16P2 compartment*Vmaxv12*(PEP*MgADP-Pyr*MgATP/Keqv12)/((PEP+KPEPv12)*(MgADP+KMgADPv12)*(1+L0v12*(1+(ATPf+MgATP)/KATPv12)^4/((1+PEP/KPEPv12)^4*(1+Fru16P2/KFru16P2v12)^4))) L0v12=19.0 dimensionless; Vmaxv12=570.0 mM_per_hour; KMgADPv12=0.474 mM; KPEPv12=0.225 mM; Keqv12=13790.0 dimensionless; KATPv12=3.39 mM; KFru16P2v12=0.005 mM