Singh2006_TCA_Ecoli_glucose

  public model
Model Identifier
BIOMD0000000222
Short description

This a model from the article:
Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drugtargets.
Singh VK , Ghosh I Theor Biol Med Model 2006 Aug 3;3:27 16887020 ,
Abstract:
BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amountof active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.

This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team.
For more information see the terms of use .
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

Format
SBML (L2V4)
Related Publication
  • Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets.
  • Singh VK, Ghosh I
  • Theoretical biology & medical modelling , 0/ 2006 , Volume 3 , pages: 27 , PubMed ID: 16887020
  • Bioinformatics Centre, University of Pune, Pune-411007, India. vivek@bioinfo.ernet.in
  • BACKGROUND: Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. RESULTS: We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i) fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii) increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. CONCLUSION: We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an approach to the assessment of drug targets is that it facilitates the study of systemic effect(s) of the modulation of the target enzyme(s) in the cellular environment.
Contributors
Indira Ghosh

Metadata information

is
BioModels Database MODEL8583955822
BioModels Database BIOMD0000000222
isDescribedBy
PubMed 16887020
hasTaxon
Taxonomy Escherichia coli
hasVersion
Gene Ontology GO:0006097
Gene Ontology GO:0006099
isHomologTo
isVersionOf

Curation status
Curated

Tags
Name Description Size Actions

Model files

BIOMD0000000222_url.xml SBML L2V4 representation of Singh2006_TCA_Ecoli_glucose 46.59 KB Preview | Download

Additional files

BIOMD0000000222_manual.png Manually generated Reaction graph (PNG) 15.47 KB Preview | Download
BIOMD0000000222_manual.svg Manually generated Reaction graph (SVG) 30.60 KB Preview | Download
BIOMD0000000222.vcml Auto-generated VCML file 50.80 KB Preview | Download
BIOMD0000000222_urn.xml Auto-generated SBML file with URNs 48.79 KB Preview | Download
BIOMD0000000222.pdf Auto-generated PDF file 197.67 KB Preview | Download
BIOMD0000000222.sci Auto-generated Scilab file 159.00 Bytes Preview | Download
BIOMD0000000222.m Auto-generated Octave file 10.61 KB Preview | Download
BIOMD0000000222-biopax2.owl Auto-generated BioPAX (Level 2) 27.05 KB Preview | Download
BIOMD0000000222-biopax3.owl Auto-generated BioPAX (Level 3) 37.68 KB Preview | Download
BIOMD0000000222.svg Auto-generated Reaction graph (SVG) 30.60 KB Preview | Download
BIOMD0000000222.png Auto-generated Reaction graph (PNG) 15.47 KB Preview | Download
BIOMD0000000222.xpp Auto-generated XPP file 6.99 KB Preview | Download

  • Model originally submitted by : Indira Ghosh
  • Submitted: Sep 29, 2006 11:47:42 PM
  • Last Modified: Dec 20, 2010 9:47:18 AM
Revisions
  • Version: 2 public model Download this version
    • Submitted on: Dec 20, 2010 9:47:18 AM
    • Submitted by: Indira Ghosh
    • With comment: Current version of Singh2006_TCA_Ecoli_glucose
  • Version: 1 public model Download this version
    • Submitted on: Sep 29, 2006 11:47:42 PM
    • Submitted by: Indira Ghosh
    • With comment: Original import of Singh_Ghosh2006_TCA_eco_glucose

(*) 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
icit

isocitric acid ; Isocitrate
0.018 mmol
akg

2-oxoglutaric acid ; 2-Oxoglutarate
0.2 mmol
sca

succinyl-CoA ; Succinyl-CoA
0.04 mmol
suc

succinic acid ; Succinate
0.6 mmol
fa

fumaric acid ; Fumarate
0.3 mmol
aca

acetyl-CoA ; Acetyl-CoA
0.5 mmol
oaa

oxaloacetic acid ; Oxaloacetate
0.004 mmol
coa

coenzyme A ; C000010
1.0E-4 mmol
Reactions
Reactions Rate Parameters
cit => icit cell*(Vf_acn*cit/Kcit_acn-Vr_acn*icit/Kicit_acn)/(1+cit/Kcit_acn+icit/Kicit_acn) Kicit_acn=3.33 mM; Vr_acn=0.912 mM_per_min; Kcit_acn=1.7 mM; Vf_acn=91.2 mM_per_min
icit => akg cell*(Vf_icd*icit/Kicit_icd-Vr_icd*akg/Kakg_icd)/(1+icit/Kicit_icd+akg/Kakg_icd) Kakg_icd=0.13 mM; Kicit_icd=0.008 mM; Vr_icd=0.1472 mM_per_min; Vf_icd=14.72 mM_per_min
akg => sca cell*(Vf_kdh*akg/Kakg_kdh-Vr_kdh*sca/Ksca_kdh)/(1+akg/Kakg_kdh+sca/Ksca_kdh) Kakg_kdh=0.1 mM; Vr_kdh=0.3584 mM_per_min; Vf_kdh=35.84 mM_per_min; Ksca_kdh=1.0 mM
akg => biosyn; icit cell*0.188*(Vf_icd*icit/Kicit_icd-Vr_icd*akg/Kakg_icd)/(1+icit/Kicit_icd+akg/Kakg_icd) Kakg_icd=0.13 mM; Kicit_icd=0.008 mM; Vr_icd=0.1472 mM_per_min; Vf_icd=14.72 mM_per_min
sca => suc cell*(Vf_scas*sca/Ksca_scas-Vr_scas*suc/Ksuc_scas)/(1+sca/Ksca_scas+suc/Ksuc_scas) Vf_scas=3.5 mM_per_min; Ksca_scas=0.02 mM; Vr_scas=0.035 mM_per_min; Ksuc_scas=5.0 mM
suc => fa cell*(Vf_sdh*suc/Ksuc_sdh-Vr_sdh*fa/Kfa_sdh)/(1+suc/Ksuc_sdh+fa/Kfa_sdh) Vr_sdh=7.31 mM_per_min; Vf_sdh=7.38 mM_per_min; Ksuc_sdh=0.02 mM; Kfa_sdh=0.4 mM
icit => suc + gly cell*(Vf_icl*icit/Kicit_icl-Vr_icl*suc/Ksuc_icl*gly/Kgly_icl)/(1+icit/Kicit_icl+suc/Ksuc_icl+gly/Kgly_icl+icit/Kicit_icl*suc/Ksuc_icl+suc/Ksuc_icl*gly/Kgly_icl) Ksuc_icl=0.59 mM; Kgly_icl=0.13 mM; Vf_icl=1.9 mM_per_min; Vr_icl=0.019 mM_per_min; Kicit_icl=0.604 mM
aca + oaa => coa + cit cell*(Vf_cs*aca/Kaca_cs*oaa/Koaa_cs-Vr_cs*coa/Kcoa_cs*cit/Kcit_cs)/((1+aca/Kaca_cs+coa/Kcoa_cs)*(1+oaa/Koaa_cs+cit/Kcit_cs)) Kaca_cs=0.03 mM; Kcit_cs=0.7 mM; Vf_cs=91.2 mM_per_min; Koaa_cs=0.07 mM; Kcoa_cs=0.3 mM; Vr_cs=0.912 mM_per_min
gly + aca => mal + coa cell*(Vf_ms*gly/Kgly_ms*aca/Kaca_ms-Vr_ms*mal/Kmal_ms*coa/Kcoa_ms)/((1+gly/Kgly_ms+mal/Kmal_ms)*(1+aca/Kaca_ms+coa/Kcoa_ms)) Kmal_ms=1.0 mM; Vf_ms=1.9 mM_per_min; Vr_ms=0.019 mM_per_min; Kgly_ms=2.0 mM; Kcoa_ms=0.1 mM; Kaca_ms=0.01 mM
mal => oaa cell*(Vf_mdh*mal/Kmal_mdh-Vr_mdh*oaa/Koaa_mdh)/(1+mal/Kmal_mdh+oaa/Koaa_mdh) Koaa_mdh=0.04 mM; Kmal_mdh=2.6 mM; Vr_mdh=353.11 mM_per_min; Vf_mdh=356.64 mM_per_min
Curator's comment:
(added: 07 Jul 2009, 16:17:50, updated: 07 Jul 2009, 16:17:50)
This model corresponds to the E.coli growth on glucose model, reported in the publication. Steady state fluxes computed based on the simulation (Table 2 - Column 2)and that compared to the experimental fluxes (Table 3 - Column 3), reported in the reference publication is reproduced here.