Holzhutter2004_Erythrocyte_Metabolism

  public model
Model Identifier
BIOMD0000000070
Short description

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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

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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.

Format
SBML (L2V1)
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.
Contributors
Nicolas Le Novère

Metadata information

is
BioModels Database MODEL6624180840
BioModels Database BIOMD0000000070
isDescribedBy
PubMed 15233787
hasTaxon
Taxonomy Homo sapiens

Curation status
Curated

Original model(s)
http://jjj.biochem.sun.ac.za/database/holzhutter/index.html

Tags
Name Description Size Actions

Model files

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

Additional files

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

  • Model originally submitted by : Nicolas Le Novère
  • Submitted: 22-Sep-2006 19:01:01
  • Last Modified: 08-Apr-2016 16:29:23
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 16:29:23
    • Submitted by: Nicolas Le Novère
    • With comment: Current version of Holzhutter2004_Erythrocyte_Metabolism
  • Version: 1 public model Download this version
    • Submitted on: 22-Sep-2006 19:01:01
    • 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.

Legends
: Variable used inside SBML models


Species
Species Initial Concentration/Amount
MgATP

magnesium atom ; ATP ; Magnesium cation ; ATP ; magnesium(2+)
1.4 mmol
NADPHf

NADPH ; NADPH
0.004 mmol
NADPf

NADP(+) ; NADP+
0.0 mmol
AMPf

AMP ; AMP
0.0 mmol
Glc6P

alpha-D-glucose 6-phosphate ; alpha-D-Glucose 6-phosphate
0.0394 mmol
Reactions
Reactions Rate Parameters
MgADP + Gri13P2 => MgATP + Gri3P compartment*Vmaxv7/(KMgADPv7*K13P2Gv7)*(MgADP*Gri13P2-MgATP*Gri3P/Keqv7)/(((1+MgADP/KMgADPv7)*(1+Gri13P2/K13P2Gv7)+(1+MgATP/KMgATPv7)*(1+Gri3P/K3PGv7))-1) Keqv7=1455.0 dimensionless; Vmaxv7=5000.0 mM_per_hour; KMgADPv7=0.35 mM; K3PGv7=1.2 mM; K13P2Gv7=0.002 mM; KMgATPv7=0.48 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
GlcA6P + NADPf => Rul5P + NADPHf; Gri23P2f, MgGri23P2, ATPf, MgATP compartment*Vmaxv18/K6PG1v18/KNADPv18*(GlcA6P*NADPf-Rul5P*NADPHf/Keqv18)/((1+NADPf/KNADPv18)*(1+GlcA6P/K6PG1v18+(Gri23P2f+MgGri23P2)/KPGA23v18)+(ATPf+MgATP)/KATPv18+NADPHf*(1+GlcA6P/K6PG2v18)/KNADPHv18) KNADPHv18=0.0045 mM; Keqv18=141.7 dimensionless; KATPv18=0.154 mM; K6PG1v18=0.01 mM; KNADPv18=0.018 mM; K6PG2v18=0.058 mM; KPGA23v18=0.12 mM; Vmaxv18=1575.0 mM_per_hour
P1NADPH => P1f + NADPHf compartment*EqMult*(P1NADPH-P1f*NADPHf/Kd3) Kd3=1.0E-5 mM; EqMult=1.0E7 hour_inverse
Pyr + NADPHf => Lac + NADPf compartment*kLDHv14*(Pyr*NADPHf-Lac*NADPf/Keqv14) Keqv14=14181.8 dimensionless; kLDHv14=243.4 per_mM_hour
P1NADP => P1f + NADPf compartment*EqMult*(P1NADP-P1f*NADPf/Kd1) EqMult=1.0E7 hour_inverse; Kd1=2.0E-4 mM
MgAMP => Mgf + AMPf compartment*EqMult*(MgAMP-Mgf*AMPf/KdAMP) KdAMP=16.64 mM; EqMult=1.0E7 hour_inverse
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
MgATP => Phi + MgADP compartment*kATPasev15*MgATP kATPasev15=1.68 hour_inverse
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 => Mgf + ATPf compartment*EqMult*(MgATP-Mgf*ATPf/KdATP) KdATP=0.072 mM; EqMult=1.0E7 hour_inverse
Glcin + MgATP => Glc6P + MgADP; Mgf, Gri23P2f, MgGri23P2 compartment*Inhibv1*Glcin/(Glcin+KMGlcv1)*Vmax1v1/KMgATPv1*((MgATP+Vmax2v1/Vmax1v1*MgATP*Mgf/KMgATPMgv1)-Glc6P*MgADP/Keqv1)/(1+MgATP/KMgATPv1*(1+Mgf/KMgATPMgv1)+Mgf/KMgv1+(1.55+Glc6P/KGlc6Pv1)*(1+Mgf/KMgv1)+(Gri23P2f+MgGri23P2)/K23P2Gv1+Mgf*(Gri23P2f+MgGri23P2)/(KMgv1*KMg23P2Gv1)) KMGlcv1=0.1 mM; Keqv1=3900.0 mM; Vmax1v1=15.8 mM_per_hour; Vmax2v1=33.2 mM_per_hour; Inhibv1=1.0 dimensionless; KMgATPv1=1.44 mM; KGlc6Pv1=0.0045 mM; K23P2Gv1=2.7 mM; KMgATPMgv1=1.14 mM; KMgv1=1.03 mM; KMg23P2Gv1=3.44 mM
Glc6P => Fru6P compartment*Vmaxv2*(Glc6P-Fru6P/Keqv2)/(Glc6P+KGlc6Pv2*(1+Fru6P/KFru6Pv2)) Keqv2=0.3925 dimensionless; KGlc6Pv2=0.182 mM; Vmaxv2=935.0 mM_per_hour; KFru6Pv2=0.071 mM
Glc6P + NADPf => GlcA6P + NADPHf; ATPf, MgATP, Gri23P2f, MgGri23P2 compartment*Vmaxv17/KG6Pv17/KNADPv17*(Glc6P*NADPf-GlcA6P*NADPHf/Keqv17)/(1+NADPf*(1+Glc6P/KG6Pv17)/KNADPv17+(ATPf+MgATP)/KATPv17+NADPHf/KNADPHv17+(Gri23P2f+MgGri23P2)/KPGA23v17) KNADPv17=0.00367 mM; KG6Pv17=0.0667 mM; Keqv17=2000.0 dimensionless; KATPv17=0.749 mM; Vmaxv17=162.0 mM_per_hour; KPGA23v17=2.289 mM; KNADPHv17=0.00312 mM