Curto1998 - purine metabolism
This is a purine metabolism model that is geared toward studies of gout.
The model uses Generalized Mass Action (GMA; i.e. power law) descriptions of reaction rate laws.
Such descriptions are local approximations that assume independent substrate binding.
This model is described in the article:
Abstract:
Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.
This model is hosted on BioModels Database and identified by: BIOMD0000000015.
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Mathematical models of purine metabolism in man.
- Curto R, Voit EO, Sorribas A, Cascante M
- Mathematical biosciences , 7/ 1998 , Volume 151 , pages: 1-49 , PubMed ID: 9664759
- Departament de Bioquímica i Biología Molecular, Facultat de Químiques, Universitat de Barcelona, Catalunya, Spain.
- Experimental and clinical data on purine metabolism are collated and analyzed with three mathematical models. The first model is the result of an attempt to construct a traditional kinetic model based on Michaelis-Menten rate laws. This attempt is only partially successful, since kinetic information, while extensive, is not complete, and since qualitative information is difficult to incorporate into this type of model. The data gaps necessitate the complementation of the Michaelis-Menten model with other functional forms that can incorporate different types of data. The most convenient and established representations for this purpose are rate laws formulated as power-law functions, and these are used to construct a Complemented Michaelis-Menten (CMM) model. The other two models are pure power-law-representations, one in the form of a Generalized Mass Action (GMA) system, and the other one in the form of an S-system. The first part of the paper contains a compendium of experimental data necessary for any model of purine metabolism. This is followed by the formulation of the three models and a comparative analysis. For physiological and moderately pathological perturbations in metabolites or enzymes, the results of the three models are very similar and consistent with clinical findings. This is an encouraging result since the three models have different structures and data requirements and are based on different mathematical assumptions. Significant enzyme deficiencies are not so well modeled by the S-system model. The CMM model captures the dynamics better, but judging by comparisons with clinical observations, the best model in this case is the GMA model. The model results are discussed in some detail, along with advantages and disadvantages of each modeling strategy.
Submitter of this revision: Nicolas Le Novère
Modellers: Nicolas Le Novère
Metadata information
BioModels Database BIOMD0000000015
Reactome REACT_522
KEGG Pathway Purine metabolism - Homo sapiens (human)
isDescribedBy (1 statement)
hasTaxon (1 statement)
isVersionOf (1 statement)
Connected external resources
SBGN view in Newt Editor
| Name | Description | Size | Actions |
|---|---|---|---|
Model files |
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| BIOMD0000000015_url.xml | SBML L2V1 representation of Curto1998 - purine metabolism | 96.80 KB | Preview | Download |
Additional files |
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| BIOMD0000000015-biopax2.owl | Auto-generated BioPAX (Level 2) | 96.66 KB | Preview | Download |
| BIOMD0000000015-biopax3.owl | Auto-generated BioPAX (Level 3) | 127.06 KB | Preview | Download |
| BIOMD0000000015.m | Auto-generated Octave file | 18.20 KB | Preview | Download |
| BIOMD0000000015.pdf | Auto-generated PDF file | 302.48 KB | Preview | Download |
| BIOMD0000000015.png | Auto-generated Reaction graph (PNG) | 588.77 KB | Preview | Download |
| BIOMD0000000015.sci | Auto-generated Scilab file | 15.90 KB | Preview | Download |
| BIOMD0000000015.svg | Auto-generated Reaction graph (SVG) | 84.93 KB | Preview | Download |
| BIOMD0000000015.vcml | Auto-generated VCML file | 874.00 Bytes | Preview | Download |
| BIOMD0000000015.xpp | Auto-generated XPP file | 12.82 KB | Preview | Download |
| BIOMD0000000015_urn.xml | Auto-generated SBML file with URNs | 92.11 KB | Preview | Download |
- Model originally submitted by : Nicolas Le Novère
- Submitted: Sep 13, 2005 2:11:12 PM
- Last Modified: Jul 2, 2014 5:48:59 PM
Revisions
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Version: 2
- Submitted on: Jul 2, 2014 5:48:59 PM
- Submitted by: Nicolas Le Novère
- With comment: Current version of Curto1998 - purine metabolism
-
Version: 1
- Submitted on: Sep 13, 2005 2:11:12 PM
- Submitted by: Nicolas Le Novère
- With comment: Original import of Curto1998_purineMetabol
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: Variable used inside SBML models
| Species | Initial Concentration/Amount |
|---|---|
| SAMP N(6)-(1,2-dicarboxyethyl)-AMP ; N6-(1,2-Dicarboxyethyl)-AMP |
0.198189 μmol |
| GTP GDP ; GMP ; GTP ; GDP ; GMP ; GDP ; GTP |
410.223 μmol |
| DNA DNA ; deoxyribonucleic acid |
5179.34 μmol |
| HX Hypoxanthine ; Deoxyinosine ; Inosine ; inosine ; hypoxanthine ; 2'-deoxyinosine |
9.51785 μmol |
| PRPP 5-O-phosphono-alpha-D-ribofuranosyl diphosphate ; 5-Phospho-alpha-D-ribose 1-diphosphate |
5.01742 μmol |
| Gua Guanine ; Deoxyguanosine ; Guanosine ; 2'-deoxyuridine ; guanine ; 2'-deoxyguanosine |
5.50638 μmol |
| IMP IMP ; IMP |
98.2634 μmol |
| dGTP dGTP ; dGMP ; dGDP ; dGTP ; dGDP ; dGMP ; dGTP |
3.02581 μmol |
| RNA RNA |
28680.5 μmol |
| Reactions | Rate | Parameters |
|---|---|---|
| SAMP => ATP; ATP | aasli*SAMP^fasli3*ATP^fasli4 | aasli=66544.0; fasli3=0.99; fasli4=-0.95 |
| RNA => GTP | arnag*RNA^frnan11 | frnan11=1.0; arnag=0.04615 |
| DNA => dATP | adnaa*DNA^fdnan12 | fdnan12=1.0; adnaa=0.001938 |
| HX + PRPP => IMP; IMP | ahprt*PRPP^fhprt1*IMP^fhprt2*HX^fhprt13 | fhprt2=-0.89; fhprt1=1.1; fhprt13=0.48; ahprt=12.569 |
| PRPP + Ade => ATP; ATP | aaprt*PRPP^faprt1*ATP^faprt4*Ade^faprt6 | aaprt=233.8; faprt4=-0.8; faprt1=0.5; faprt6=0.75 |
| HX => Xa | ahxd*HX^fhxd13 | fhxd13=0.65; ahxd=0.2754 |
| GTP => Gua; Pi | agnuc*GTP^fgnuc8*Pi^fgnuc18 | fgnuc18=-0.34; agnuc=0.2511; fgnuc8=0.9 |
| GTP => dGTP; dATP, dGTP | agdrnr*GTP^fgdrnr8*dATP^fgdrnr9*dGTP^fgdrnr10 | fgdrnr10=-0.39; agdrnr=0.1199; fgdrnr8=0.4; fgdrnr9=-1.2 |
| ATP => RNA; GTP | aarna*ATP^frnap4*GTP^frnap8 | aarna=614.5; frnap4=0.05; frnap8=0.13 |
(added: 02 Jun 2008, 14:14:13, updated: 02 Jun 2008, 14:14:13)