Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation

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Model Identifier
BIOMD0000000725
Short description
Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation

This model is described in the article:

Enrique Salcedo-Sora J, Mc Auley MT.
Mol Biosyst. 2016 Jan 21.

Abstract:

The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.

To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Format
SBML (L2V4)
Related Publication
  • A mathematical model of microbial folate biosynthesis and utilisation: implications for antifolate development.
  • Salcedo-Sora JE
  • Molecular bioSystems , 3/ 2016 , Volume 12 , pages: 923-933 , PubMed ID: 26794619
  • School of Health Science, Liverpool Hope University, Hope Park, Liverpool, L16 9JD, UK. salcede@hope.a.uk.
  • The metabolic biochemistry of folate biosynthesis and utilisation has evolved into a complex network of reactions. Although this complexity represents challenges to the field of folate research it has also provided a renewed source for antimetabolite targets. A range of improved folate chemotherapy continues to be developed and applied particularly to cancer and chronic inflammatory diseases. However, new or better antifolates against infectious diseases remain much more elusive. In this paper we describe the assembly of a generic deterministic mathematical model of microbial folate metabolism. Our aim is to explore how a mathematical model could be used to explore the dynamics of this inherently complex set of biochemical reactions. Using the model it was found that: (1) a particular small set of folate intermediates are overrepresented, (2) inhibitory profiles can be quantified by the level of key folate products, (3) using the model to scan for the most effective combinatorial inhibitions of folate enzymes we identified specific targets which could complement current antifolates, and (4) the model substantiates the case for a substrate cycle in the folinic acid biosynthesis reaction. Our model is coded in the systems biology markup language and has been deposited in the BioModels Database (MODEL1511020000), this makes it accessible to the community as a whole.
Contributors
Submitter of the first revision: J. Enrique Salcedo-Sora
Submitter of this revision: Sarubini Kananathan
Modellers: J. Enrique Salcedo-Sora, Sarubini Kananathan

Metadata information

is (3 statements)
BioModels Database MODEL1511020000
BioModels Database MODEL1511020000
BioModels Database BIOMD0000000725

hasProperty (5 statements)
Mathematical Modelling Ontology Ordinary differential equation model
Experimental Factor Ontology infectious disease
NCIt Folate Biosynthesis Pathway
NCIt Microorganism
Pathway Ontology shikimate metabolic pathway

isDescribedBy (1 statement)

Curation status
Curated


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Name Description Size Actions

Model files

Model.xml SBML L2V4 representation of Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation 204.69 KB Preview | Download

Additional files

MODEL1511020000.pdf Auto-generated PDF file 406.69 KB Preview | Download
MODEL1511020000.png Auto-generated Reaction graph (PNG) 1.26 MB Preview | Download
MODEL1511020000.sci Auto-generated Scilab file 15.89 KB Preview | Download
MODEL1511020000.svg Auto-generated Reaction graph (SVG) 122.10 KB Preview | Download
MODEL1511020000.vcml Auto-generated VCML file 176.64 KB Preview | Download
MODEL1511020000_urn.xml Auto-generated SBML file with URNs 167.02 KB Preview | Download

  • Model originally submitted by : J. Enrique Salcedo-Sora
  • Submitted: Nov 2, 2015 2:19:42 PM
  • Last Modified: Dec 5, 2018 11:01:18 AM
Revisions
  • Version: 4 public model Download this version
    • Submitted on: Dec 5, 2018 11:01:18 AM
    • Submitted by: Sarubini Kananathan
    • With comment: Automatically added model identifier BIOMD0000000725
  • Version: 2 public model Download this version
    • Submitted on: Feb 10, 2016 11:20:53 AM
    • Submitted by: J. Enrique Salcedo-Sora
    • With comment: Current version of Salcedo-Sora2016 - Microbial folate biosynthesis and utilisation
  • Version: 1 public model Download this version
    • Submitted on: Nov 2, 2015 2:19:42 PM
    • Submitted by: J. Enrique Salcedo-Sora
    • With comment: Original import of New Model

(*) 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
Pi

Orthophosphate
2.725541316 μmol
CM

Chorismate
1.009195849 μmol
meTHFGlu

5,10-Methenyltetrahydrofolate
0.9082384182 μmol
COTwo 0.988683328 μmol
ATP

ATP
963.0188351 μmol
DHQ

3-Dehydroquinate
0.9994087764 μmol
AHMDPP

6-Hydroxymethyl-7,8-dihydropterin diphosphate
0.9873083466 μmol
THFGlu

THF-polyglutamate
1.0 μmol
Reactions
Reactions Rate Parameters
DHP + Glu + ATP => DHF + ADP + Pi; DHP, Glu, ATP compartment*vmax*DHP*Glu*ATP/(kdhp*kglu*katp+kdhp*(Glu+ATP)+kglu*(DHP+ATP)+katp*(Glu+ATP)+DHP*Glu*ATP) vmax=2.821; kglu=1380.0; katp=100.0; kdhp=1.0
AHMDPP + pABA => DHP + Pi; AHMDPP, pABA compartment*vmax*AHMDPP*pABA/(kahmdpp*kpaba+kpaba*AHMDPP+kahmdpp*pABA+AHMDPP*pABA) kpaba=2.6; vmax=105.014; kahmdpp=3.15
CVPSK => CM + Pi; CVPSK compartment*V*CVPSK/(Km+CVPSK) Km=12.7; V=728.0
ATP + ffTHFGlu => ADP + Pi + meTHFGlu; ATP, ffTHFGlu compartment*vmax*ATP*ffTHFGlu/(katp*kffthfglu+katp*ffTHFGlu+kffthfglu*ATP+ATP*ffTHFGlu) kffthfglu=5.0; katp=50.0; vmax=500.0
DLp + Gly => SAmDLp + COTwo; DLp, Gly compartment*vmax*DLp*Gly/(kgly*kdlp+kgly*Gly+kdlp*DLp+DLp*Gly) kdlp=290.0; kgly=4505.0; vmax=751.66
THF + Glu + ATP => THFGlu + ADP + Pi; DHF, THF, Glu, ATP compartment*vmax*THF*Glu*ATP/(kthf*(1+DHF/kidhf)*kglu*katp+kthf*(Glu+ATP)+kglu*(THF+ATP)+katp*(THF+Glu)+THF*Glu*ATP) kthf=26.0; vmax=84.63; kglu=740.0; katp=128.0; kidhf=3.1
SKP + PEP => CVPSK + Pi; SKP, PEP compartment*vmax*SKP*PEP/(kpep*kskp+kpep*PEP+kskp*SKP+PEP*SKP) kpep=93.0; kskp=80.0; vmax=1547.0
DAHP => DHQ + Pi; DAHP compartment*V*DAHP/(Km+DAHP) Km=4.7; V=7.462
fTHFGlu + ADP + Pi => THFGlu + ATP + Formyl; fTHFGlu, ADP, Pi compartment*vmax*fTHFGlu*ADP*Pi/(kthfglu*kformyl*katp+kthfglu*(ADP+Pi)+kformyl*(fTHFGlu+Pi)+katp*(ADP+fTHFGlu)+fTHFGlu*ADP*Pi) vmax=15315.3; kformyl=3190.0; kthfglu=134.0; katp=74.5
Curator's comment:
(added: 05 Dec 2018, 11:00:38, updated: 05 Dec 2018, 11:00:38)
Figure 2 of the reference publication has been reproduced. Initial conditions and values were taken from the supplementary file attached to the publication. The model was simulated using Copasi 4.24 and the figure was generated using Python 3.7.