Holzhutter2004_Erythrocyte_Metabolism
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
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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
Submitter of the first revision: Nicolas Le Novère
Submitter of this revision: Nicolas Le Novère
Modellers: Nicolas Le Novère
Submitter of this revision: Nicolas Le Novère
Modellers: Nicolas Le Novère
Metadata information
is
isDescribedBy
hasTaxon
hasPart
Gene Ontology
pentose-phosphate shunt
Gene Ontology glutathione metabolic process
Gene Ontology glycolytic process
KEGG Pathway Pentose phosphate pathway - Homo sapiens (human)
KEGG Pathway Glycolysis / Gluconeogenesis - Homo sapiens (human)
KEGG Pathway Glutathione metabolism - Homo sapiens (human)
Reactome Glycolysis
Reactome Pentose phosphate pathway (hexose monophosphate shunt)
Reactome glutathione (oxidized) + NADPH + H+ => 2 glutathione (reduced) + NADP+
Gene Ontology glutathione metabolic process
Gene Ontology glycolytic process
KEGG Pathway Pentose phosphate pathway - Homo sapiens (human)
KEGG Pathway Glycolysis / Gluconeogenesis - Homo sapiens (human)
KEGG Pathway Glutathione metabolism - Homo sapiens (human)
Reactome Glycolysis
Reactome Pentose phosphate pathway (hexose monophosphate shunt)
Reactome glutathione (oxidized) + NADPH + H+ => 2 glutathione (reduced) + NADP+
Curation status
Curated
Original model(s)
http://jjj.biochem.sun.ac.za/database/holzhutter/index.html
Tags
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| BIOMD0000000070_url.xml | SBML L2V1 representation of Holzhutter2004_Erythrocyte_Metabolism | 161.83 KB | Preview | Download |
Additional files |
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| 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
Revisions
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Version: 2
- Submitted on: Apr 8, 2016 4:29:23 PM
- Submitted by: Nicolas Le Novère
- With comment: Current version of Holzhutter2004_Erythrocyte_Metabolism
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Version: 1
- Submitted on: Sep 22, 2006 7:01:01 PM
- Submitted by: Nicolas Le Novère
- With comment: Original import of holzhutter
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revisions as only public revisions are displayed here. Any private revisions
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Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
| Species | Initial Concentration/Amount |
|---|---|
| MgATP magnesium atom ; ATP ; Magnesium cation ; ATP ; magnesium(2+) |
1.4 mmol |
| MgADP magnesium atom ; ADP ; Magnesium cation ; ADP ; magnesium(2+) |
0.1 mmol |
| GraP D-glyceraldehyde 3-phosphate ; D-Glyceraldehyde 3-phosphate |
0.0061 mmol |
| Sed7P Sedoheptulose 7-phosphate ; sedoheptulose 7-phosphate |
0.0154 mmol |
| MgAMP magnesium atom ; AMP ; Magnesium cation ; AMP ; magnesium(2+) |
0.0 mmol |
| NADH NADH ; NADH |
2.0E-4 mmol |
| Gri2P 2-phospho-D-glyceric acid ; 2-Phospho-D-glycerate |
0.0084 mmol |
| PEP Phosphoenolpyruvate ; phosphoenolpyruvate ; phosphoenolpyruvate |
0.0109 mmol |
| Pyr pyruvate ; Pyruvate ; pyruvic acid |
0.084 mmol |
Reactions
| 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 |