BioModels Database logo

BioModels Database

spacer

BIOMD0000000428 - Achcar2012 - Glycolysis in bloodstream form T. brucei

 

 |   |   |  Send feedback
Reference Publication
Publication ID: 22379410
Achcar F, Kerkhoven EJ, SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
Dynamic modelling under uncertainty: the case of Trypanosoma brucei energy metabolism.
PLoS Comput. Biol. 2012 Jan; 8(1): e1002352
Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom.  [more]
Model
Original Model: http://www.ploscompbiol.or...
Submitter: Lukas Endler
Submission ID: MODEL1209130000
Submission Date: 13 Sep 2012 11:20:16 UTC
Last Modification Date: 03 Sep 2014 16:23:37 UTC
Creation Date: 20 Nov 2012 18:36:29 UTC
Encoders:  Vijayalakshmi Chelliah
   Lukas Endler
set #1
bqbiol:occursIn Taxonomy Trypanosoma brucei
bqbiol:isVersionOf Gene Ontology glycolytic process
Notes
Achcar2012 - Glycolysis in bloodstream form T. brucei

Kinetic models of metabolism require quantitative knowledge of detailed kinetic parameters. However, the knowledge about these parameters is often uncertain. An analysis of the effect of parameter uncertainties on a particularly well defined example of a quantitative metablic model, the model of glycolysis in bloodstream form Trypanosoma brucei , has been presented here.

This model is described in the article:

Achcar F, Kerkhoven EJ; SilicoTryp Consortium, Bakker BM, Barrett MP, Breitling R.
PLoS Comput Biol. 2012 Jan; 8(1):e1002352.

Abstract:

Kinetic models of metabolism require detailed knowledge of kinetic parameters. However, due to measurement errors or lack of data this knowledge is often uncertain. The model of glycolysis in the parasitic protozoan Trypanosoma brucei is a particularly well analysed example of a quantitative metabolic model, but so far it has been studied with a fixed set of parameters only. Here we evaluate the effect of parameter uncertainty. In order to define probability distributions for each parameter, information about the experimental sources and confidence intervals for all parameters were collected. We created a wiki-based website dedicated to the detailed documentation of this information: the SilicoTryp wiki (http://silicotryp.ibls.gla.ac.uk/wiki/Glycolysis). Using information collected in the wiki, we then assigned probability distributions to all parameters of the model. This allowed us to sample sets of alternative models, accurately representing our degree of uncertainty. Some properties of the model, such as the repartition of the glycolytic flux between the glycerol and pyruvate producing branches, are robust to these uncertainties. However, our analysis also allowed us to identify fragilities of the model leading to the accumulation of 3-phosphoglycerate and/or pyruvate. The analysis of the control coefficients revealed the importance of taking into account the uncertainties about the parameters, as the ranking of the reactions can be greatly affected. This work will now form the basis for a comprehensive Bayesian analysis and extension of the model considering alternative topologies.

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.

Model
Publication ID: 22379410 Submission Date: 13 Sep 2012 11:20:16 UTC Last Modification Date: 03 Sep 2014 16:23:37 UTC Creation Date: 20 Nov 2012 18:36:29 UTC
Mathematical expressions
Reactions
GlyT_g PYK_c GlyT_c GlcT_g
PyrT_c GlcT_c PFK_g ENO_c
HXK_g _3PGAT_g PGK_g PGAM_c
G3PDH_g ATPu_c GK_g ALD_g
AK_c PGI_g GAPDH_g AK_g
GDA_g GPO_c TPI_g  
Physical entities
Compartments Species
cytosol _2PGA_c ATP_c Pyr_c
DHAP_c Gly3P_c ADP_c
_3PGA_c Gly_c AMP_c
PEP_c Glc_c  
glycosome ATP_g DHAP_g NAD_g
Glc6P_g Fru16BP_g Gly_g
Fru6P_g _3PGA_g Pi_g
GA3P_g Gly3P_g ADP_g
AMP_g _13BPGA_g Glc_g
NADH_g    
default Pyr_e Gly_e O2_c
Glc_e    
Reactions (23)
 
 GlyT_g [Gly_g] ↔ [Gly_c];   {Gly_g} , {Gly_c}
 
 PYK_c [PEP_c] + [ADP_c] ↔ [Pyr_c] + [ATP_c];   {ADP_c} , {PEP_c} , {ATP_c}
 
 GlyT_c [Gly_c] ↔ [Gly_e];   {Gly_c} , {Gly_e}
 
 GlcT_g [Glc_c] ↔ [Glc_g];   {Glc_c} , {Glc_g}
 
 PyrT_c [Pyr_c] ↔ [Pyr_e];   {Pyr_c}
 
 GlcT_c [Glc_e] ↔ [Glc_c];   {Glc_e} , {Glc_c}
 
 PFK_g [ATP_g] + [Fru6P_g] ↔ [Fru16BP_g] + [ADP_g];   {ATP_g} , {Fru6P_g} , {Fru16BP_g}
 
 ENO_c [_2PGA_c] ↔ [PEP_c];   {_2PGA_c} , {PEP_c}
 
 HXK_g [ATP_g] + [Glc_g] ↔ [Glc6P_g] + [ADP_g];   {ATP_g} , {Glc_g} , {Glc6P_g} , {ADP_g}
 
 _3PGAT_g [_3PGA_g] ↔ [_3PGA_c];   {_3PGA_g} , {_3PGA_c}
 
 PGK_g [_13BPGA_g] + [ADP_g] ↔ [_3PGA_g] + [ATP_g];   {_13BPGA_g} , {ADP_g} , {_3PGA_g} , {ATP_g}
 
 PGAM_c [_3PGA_c] ↔ [_2PGA_c];   {_3PGA_c} , {_2PGA_c}
 
 G3PDH_g [NADH_g] + [DHAP_g] ↔ [Gly3P_g] + [NAD_g];   {DHAP_g} , {NADH_g} , {Gly3P_g} , {NAD_g}
 
 ATPu_c [ATP_c] ↔ [ADP_c];   {ATP_c} , {ADP_c}
 
 GK_g [Gly3P_g] + [ADP_g] ↔ [Gly_g] + [ATP_g];   {Gly3P_g} , {ADP_g} , {Gly_g} , {ATP_g}
 
 ALD_g [Fru16BP_g] ↔ [GA3P_g] + [DHAP_g];   {ATP_g} , {ADP_g} , {AMP_g} , {Fru16BP_g} , {GA3P_g} , {DHAP_g} , {ATP_g} , {ADP_g} , {AMP_g}
 
 AK_c 2.0 × [ADP_c] ↔ [AMP_c] + [ATP_c];   {ADP_c} , {AMP_c} , {ATP_c}
 
 PGI_g [Glc6P_g] ↔ [Fru6P_g];   {Glc6P_g} , {Fru6P_g}
 
 GAPDH_g [GA3P_g] + [NAD_g] + [Pi_g] ↔ [NADH_g] + [_13BPGA_g];   {GA3P_g} , {NAD_g} , {_13BPGA_g} , {NADH_g}
 
 AK_g 2.0 × [ADP_g] ↔ [AMP_g] + [ATP_g];   {ADP_g} , {AMP_g} , {ATP_g}
 
 GDA_g [Gly3P_g] + [DHAP_c] ↔ [Gly3P_c] + [DHAP_g];   {Gly3P_g} , {DHAP_c} , {Gly3P_c} , {DHAP_g}
 
 GPO_c [Gly3P_c] ↔ [DHAP_c];   {Gly3P_c}
 
 TPI_g [DHAP_g] ↔ [GA3P_g];   {DHAP_g} , {GA3P_g}
 
Functions (5)
 
 mass_action_rev lambda(k1, S, k2, P, k1*S-k2*P)
 
 vAK lambda(ADP, AMP, ATP, k1, k2, k1*ADP^2-AMP*ATP*k2)
 
 v1sub1prod lambda(Vfmax, Keq, S, Ks, P, Kp, Vfmax*S*(1-P/(Keq*S))/(Ks*(1+S/Ks+P/Kp)))
 
 v1sub lambda(Vfmax, S, Ks, Vfmax*S/(Ks*(1+S/Ks)))
 
 v2sub2prod lambda(Vfmax, Keq, S1, Ks1, S2, Ks2, P1, Kp1, P2, Kp2, Vfmax*S1*S2*(1-P1*P2/(Keq*S1*S2))/(Ks1*Ks2*(1+S2/Ks2+P2/Kp2)*(1+S1/Ks1+P1/Kp1)))
 
 cytosol Spatial dimensions: 3.0  Compartment size: 5.4549
 
 _2PGA_c
Compartment: cytosol
Initial concentration: 0.1
 
 ATP_c
Compartment: cytosol
Initial concentration: 0.3417
 
 Pyr_c
Compartment: cytosol
Initial concentration: 10.0
 
 DHAP_c
Compartment: cytosol
Initial concentration: 2.23132912
 
 Gly3P_c
Compartment: cytosol
Initial concentration: 2.76867088
 
 ADP_c
Compartment: cytosol
Initial concentration: 1.3165
 
 _3PGA_c
Compartment: cytosol
Initial concentration: 0.1
 
 Gly_c
Compartment: cytosol
Initial concentration: 1.0
 
 AMP_c
Compartment: cytosol
Initial concentration: 2.2418
 
 PEP_c
Compartment: cytosol
Initial concentration: 1.0
 
 Glc_c
Compartment: cytosol
Initial concentration: 0.01
 
 glycosome Spatial dimensions: 3.0  Compartment size: 0.2451
 
 ATP_g
Compartment: glycosome
Initial concentration: 0.2405
 
 DHAP_g
Compartment: glycosome
Initial concentration: 8.483130623
 
 NAD_g
Compartment: glycosome
Initial concentration: 2.0
 
 Glc6P_g
Compartment: glycosome
Initial concentration: 0.5
 
 Fru16BP_g
Compartment: glycosome
Initial concentration: 10.0
 
 Gly_g
Compartment: glycosome
Initial concentration: 1.0
 
 Fru6P_g
Compartment: glycosome
Initial concentration: 0.5
 
 _3PGA_g
Compartment: glycosome
Initial concentration: 0.1
 
 Pi_g
Compartment: glycosome
Initial concentration: 0.0
Constant
 
 GA3P_g
Compartment: glycosome
Initial concentration: 2.5
 
 Gly3P_g
Compartment: glycosome
Initial concentration: 10.51686938
 
 ADP_g
Compartment: glycosome
Initial concentration: 1.519
 
 AMP_g
Compartment: glycosome
Initial concentration: 4.2405
 
 _13BPGA_g
Compartment: glycosome
Initial concentration: 0.5
 
 Glc_g
Compartment: glycosome
Initial concentration: 0.01
 
 NADH_g
Compartment: glycosome
Initial concentration: 2.0
 
 default Spatial dimensions: 3.0  Compartment size: 1.0
 
 Pyr_e
Compartment: default
Initial concentration: 0.0
Constant
 
 Gly_e
Compartment: default
Initial concentration: 0.0
Constant
 
 O2_c
Compartment: default
Initial concentration: 1.0
Constant
 
 Glc_e
Compartment: default
Initial concentration: 5.0
Constant
 
GlyT_g (1)
 
   GlyT_g_k
Value: 9000.0
Constant
 
PYK_c (6)
 
   PYK_c_Vmax
Value: 1020.0
Constant
 
   PYK_c_KmPEP
Value: 0.34
Constant
 
   PYK_c_KiATP
Value: 0.57
Constant
 
   PYK_c_KiADP
Value: 0.64
Constant
 
   PYK_c_n
Value: 2.5
Constant
 
   PYK_c_KmADP
Value: 0.114
Constant
 
GlyT_c (3)
 
   GlyT_c_Vmax
Value: 85.0
Constant
 
   GlyT_c_KmGly
Value: 0.17
Constant
 
   GlyT_c_k
Value: 9.0
Constant
 
GlcT_g (1)
 
   GlcT_g_k
Value: 250000.0
Constant
 
PyrT_c (2)
 
   PyrT_c_Vmax
Value: 200.0
Constant
 
   PyrT_c_KmPyr
Value: 1.96
Constant
 
GlcT_c (3)
 
   GlcT_c_Vmax
Value: 108.9
Constant
 
   GlcT_c_KmGlc
Value: 1.0
Constant
 
   GlcT_c_alpha
Value: 0.75
Constant
 
PFK_g (5)
 
   PFK_g_Vmax
Value: 1708.0
Constant
 
   PFK_g_Ki1
Value: 15.8
Constant
 
   PFK_g_KmFru6P
Value: 0.82
Constant
 
   PFK_g_KmATP
Value: 0.026
Constant
 
   PFK_g_Ki2
Value: 10.7
Constant
 
ENO_c (4)
 
   ENO_c_Vmax
Value: 598.0
Constant
 
   ENO_c_Keq
Value: 6.7
Constant
 
   ENO_c_Km2PGA
Value: 0.054
Constant
 
   ENO_c_KmPEP
Value: 0.24
Constant
 
HXK_g (5)
 
   HXK_g_Vmax
Value: 1929.0
Constant
 
   HXK_g_KmGlc
Value: 0.1
Constant
 
   HXK_g_KmATP
Value: 0.116
Constant
 
   HXK_g_KmADP
Value: 0.126
Constant
 
   HXK_g_KmGlc6P
Value: 12.0
Constant
 
_3PGAT_g (1)
 
   _3PGAT_g_k
Value: 250.0
Constant
 
PGK_g (6)
 
   PGK_g_Vmax
Value: 2862.0
Constant
 
   PGK_g_Keq
Value: 3332.0
Constant
 
   PGK_g_Km13BPGA
Value: 0.0030
Constant
 
   PGK_g_KmADP
Value: 0.1
Constant
 
   PGK_g_Km3PGA
Value: 1.62
Constant
 
   PGK_g_KmATP
Value: 0.29
Constant
 
PGAM_c (4)
 
   PGAM_c_Vmax
Value: 225.0
Constant
 
   PGAM_c_Keq
Value: 0.185
Constant
 
   PGAM_c_Km3PGA
Value: 0.15
Constant
 
   PGAM_c_Km2PGA
Value: 0.16
Constant
 
G3PDH_g (6)
 
   G3PDH_g_Vmax
Value: 465.0
Constant
 
   G3PDH_g_Keq
Value: 2857.0
Constant
 
   G3PDH_g_KmDHAP
Value: 0.1
Constant
 
   G3PDH_g_KmNADH
Value: 0.01
Constant
 
   G3PDH_g_KmGly3P
Value: 2.0
Constant
 
   G3PDH_g_KmNAD
Value: 0.4
Constant
 
ATPu_c (1)
 
   ATPu_c_k
Value: 50.0
Constant
 
GK_g (6)
 
   GK_g_Vmax
Value: 200.0
Constant
 
   GK_g_Keq
Value: 8.0E-4
Constant
 
   GK_g_KmGly3P
Value: 3.83
Constant
 
   GK_g_KmADP
Value: 0.56
Constant
 
   GK_g_KmGly
Value: 0.44
Constant
 
   GK_g_KmATP
Value: 0.24
Constant
 
ALD_g (9)
 
   ALD_g_Vmax
Value: 560.0
Constant
 
   ALD_g_KmFru16BP
Value: 0.0090
Constant
 
   ALD_g_KiATP
Value: 0.68
Constant
 
   ALD_g_KiADP
Value: 1.51
Constant
 
   ALD_g_KiAMP
Value: 3.65
Constant
 
   ALD_g_Keq
Value: 0.093
Constant
 
   ALD_g_KmGA3P
Value: 0.067
Constant
 
   ALD_g_KmDHAP
Value: 0.015
Constant
 
   ALD_g_KiGA3P
Value: 0.098
Constant
 
AK_c (2)
 
   AK_c_k1
Value: 442.0
Constant
 
   AK_c_k2
Value: 1000.0
Constant
 
PGI_g (4)
 
   PGI_g_Vmax
Value: 1305.0
Constant
 
   PGI_g_Keq
Value: 0.3
Constant
 
   PGI_g_KmGlc6P
Value: 0.4
Constant
 
   PGI_g_KmFru6P
Value: 0.12
Constant
 
GAPDH_g (6)
 
   GAPDH_g_Vmax
Value: 720.9
Constant
 
   GAPDH_g_Keq
Value: 0.044
Constant
 
   GAPDH_g_KmGA3P
Value: 0.15
Constant
 
   GAPDH_g_KmNAD
Value: 0.45
Constant
 
   GAPDH_g_Km13BPGA
Value: 0.1
Constant
 
   GAPDH_g_KmNADH
Value: 0.02
Constant
 
AK_g (2)
 
   AK_g_k1
Value: 442.0
Constant
 
   AK_g_k2
Value: 1000.0
Constant
 
GDA_g (1)
 
   GDA_g_k
Value: 600.0
Constant
 
GPO_c (2)
 
   GPO_c_Vmax
Value: 368.0
Constant
 
   GPO_c_KmGly3P
Value: 1.7
Constant
 
TPI_g (4)
 
   TPI_g_Vmax
Value: 999.3
Constant
 
   TPI_g_Keq
Value: 0.045
Constant
 
   TPI_g_KmDHAP
Value: 1.2
Constant
 
   TPI_g_KmGA3P
Value: 0.25
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000428

Curator's comment: (updated: 20 Nov 2012 11:00:45 GMT)

As there are no figures that correspond to this fixed-parameter model, the steady state concentrations of four metabolites are highlighted in the table. Figure 5 of the paper shows the distribution of the steady-state concentrations of these four metabolites, where the vertical black line in the plots indicate the value for the fixed-parameter model (highlighted in the table here).

The model was simulated using Copasi v4.8 (Build 35).

spacer
spacer