Reed2008_Glutathione_Metabolism

  public model
Short description

This is the model described in the article:
A mathematical model of glutathione metabolism.
Michael C Reed, Rachel L Thomas, Jovana Pavisic, S. Jill James, Cornelia M Ulrich and H. Frederik Nijhout, Theor Biol Med Model 2008,5:8; PubmedID:18442411 ; DOI:10.1186/1742-4682-5-8;
Abstract:
BACKGROUND: Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.
APPROACH: We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.
CONCLUSION: We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.

parameter orig. article this model
Vm_CBS 700000 420000
Vm_GNMT 245 260
K_sam_GNMT 32 63
Vr_MTD(mito) 600000 595000
V_CBS kinetic law rearranged
V_bmetc 913 913.4
Vm_GR 8925 892.5

This version of the model contains a feeding rhythm as used in figure 5 of the original article. Four parameters, breakfast, lunch dinner and fasting, describe the relative level of amino acids, described by the parameter aa_input or Aminoacid_input, in the blood. To remove the daily feeding rhythm, either set the parameters for meals and fasting to 1 (or for figure 3 to 0.333), or remove the assignment rule for the Aminoacid_input. For the steady state evaluations for figure 6, the mealtime parameters were set to one, which, while making Copasi complain about explicit time dependency, still gives valid results.

This version of the model differs slightly from the version described in the supplement, in which contains some typos. It was corrected using the version of JWS-online, created using the original matlab files, thankfully provided by the articles authors. Many thanks to Jacky Snoep for his help and support.

In the SBML version of the model the volumes of the mitochondrion, the cytoplasm and the cell were all set to one to obtain the same equations as described in the supplemental materials of the article. The total folate is equally split between the cytosol and the mitochondrion and divided by 3/4 for the cytosol and 1/4 for the mitochondrion, respectively. To obtain an SBML model in which the volumes of the compartments, cytosol and mito, are used, the model needs to be altered as follows:

  • for the initial distribution of folate the terms 3/4 and 1/4 have to be replaced by volumes of cytosol and mitochondria respectively
  • in the transport reactions between mitochondrion and cytosol the stoichiometry of the mitochondrial reactants has to be set from 3 to 1 and in the first part of the according rate laws the factor mito/3 should simply be replaced with mito.
  • the stoichiometries of src and dmg have to be changed to cell/mito for mitchondrial and cell/cytosol for cytosolic reactions involving these two species (for the relative volumes used in the article this would be 4 for mitochondrial reactions and 1.33333 for cytosolic ones).
While the concentrations stay the same after these alteration, the reaction fluxes change by a factor of cytosol and mito for cytosolic and mitchondrial reactions, respectively.

Originally created by libAntimony v1.3 (using libSBML 3.4.1)

Format
SBML (L2V4)
Related Publication
  • A mathematical model of glutathione metabolism.
  • Reed MC, Thomas RL, Pavisic J, James SJ, Ulrich CM, Nijhout HF
  • Theoretical biology & medical modelling , 4/ 2008 , Volume 5 , pages: 8
  • Department of Mathematics, Duke University, Durham, NC 27708, USA. reed@math.duke.edu
  • Glutathione (GSH) plays an important role in anti-oxidant defense and detoxification reactions. It is primarily synthesized in the liver by the transsulfuration pathway and exported to provide precursors for in situ GSH synthesis by other tissues. Deficits in glutathione have been implicated in aging and a host of diseases including Alzheimer's disease, Parkinson's disease, cardiovascular disease, cancer, Down syndrome and autism.We explore the properties of glutathione metabolism in the liver by experimenting with a mathematical model of one-carbon metabolism, the transsulfuration pathway, and glutathione synthesis, transport, and breakdown. The model is based on known properties of the enzymes and the regulation of those enzymes by oxidative stress. We explore the half-life of glutathione, the regulation of glutathione synthesis, and its sensitivity to fluctuations in amino acid input. We use the model to simulate the metabolic profiles previously observed in Down syndrome and autism and compare the model results to clinical data.We show that the glutathione pools in hepatic cells and in the blood are quite insensitive to fluctuations in amino acid input and offer an explanation based on model predictions. In contrast, we show that hepatic glutathione pools are highly sensitive to the level of oxidative stress. The model shows that overexpression of genes on chromosome 21 and an increase in oxidative stress can explain the metabolic profile of Down syndrome. The model also correctly simulates the metabolic profile of autism when oxidative stress is substantially increased and the adenosine concentration is raised. Finally, we discuss how individual variation arises and its consequences for one-carbon and glutathione metabolism.
Contributors
Lukas Endler

Metadata information

is
BioModels Database MODEL1007200001
BioModels Database BIOMD0000000268
isDerivedFrom
BioModels Database MODEL1007200000
isDescribedBy
PubMed 18442411
hasTaxon
Taxonomy Homo sapiens
Brenda Tissue Ontology hepatocyte
isVersionOf
hasProperty
Human Disease Ontology Parkinson's disease
Human Disease Ontology cardiovascular system disease
Human Disease Ontology autistic disorder
Human Disease Ontology Down syndrome
Human Disease Ontology Alzheimer's disease
Human Disease Ontology cancer
Curation status
Curated
Name Description Size Actions

Model files

BIOMD0000000268_url.xml SBML L2V4 representation of Reed2008_Glutathione_Metabolism 143.85 KB Preview | Download

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  • Model originally submitted by : Lukas Endler
  • Submitted: 20-Jul-2010 15:33:55
  • Last Modified: 08-Apr-2016 17:17:49
Revisions
  • Version: 2 public model Download this version
    • Submitted on: 08-Apr-2016 17:17:49
    • Submitted by: Lukas Endler
    • With comment: Current version of Reed2008_Glutathione_Metabolism
  • Version: 1 public model Download this version
    • Submitted on: 20-Jul-2010 15:33:55
    • Submitted by: Lukas Endler
    • With comment: Original import of Reed2008_Glutathione_Metabolism
Curator's comment:
(added: 07 Sep 2010, 17:27:43, updated: 07 Sep 2010, 17:27:43)
Reproduction of figure 5 of the original article. Only the second day was chosen to be displayed, as the initial concentrations for the time courses are not known.
The calculations were performed using Copasi 4.6.