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BIOMD0000000585 - Rateitschak2012 - Interferon-gamma (IFN?) induced STAT1 signalling (PC_IFNg100)

 

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Reference Publication
Publication ID: 23284277
Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O.
Parameter identifiability and sensitivity analysis predict targets for enhancement of STAT1 activity in pancreatic cancer and stellate cells.
PLoS Comput. Biol. 2012; 8(12): e1002815
Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany. katja.rateitschak@uni-rostock.de  [more]
Model
Original Model: http://files.kapora.de/Rat...
Submitter: Felix Winter
Submission ID: MODEL1509240000
Submission Date: 24 Sep 2015 12:05:46 UTC
Last Modification Date: 16 Feb 2016 12:13:38 UTC
Creation Date: 14 Mar 2015 08:22:46 UTC
Encoders:  Vijayalakshmi Chelliah
   Felix Winter
   Katja Rateitschak
set #1
bqbiol:hasProperty Human Disease Ontology DOID:1793
set #2
bqbiol:isVersionOf Gene Ontology JAK-STAT cascade
Gene Ontology response to interferon-gamma
set #3
bqbiol:occursIn Cell Type Ontology CL:0002410
Brenda Tissue Ontology BTO:0000584
set #4
bqbiol:hasTaxon Taxonomy Rattus
Notes
Rateitschak2012 - Interferon-gamma (IFNγ) induced STAT1 signalling (PC_IFNg100)

This model is described in the article:

Rateitschak K, Winter F, Lange F, Jaster R, Wolkenhauer O.
PLoS Comput. Biol. 2012; 8(12): e1002815

Abstract:

The present work exemplifies how parameter identifiability analysis can be used to gain insights into differences in experimental systems and how uncertainty in parameter estimates can be handled. The case study, presented here, investigates interferon-gamma (IFNγ) induced STAT1 signalling in two cell types that play a key role in pancreatic cancer development: pancreatic stellate and cancer cells. IFNγ inhibits the growth for both types of cells and may be prototypic of agents that simultaneously hit cancer and stroma cells. We combined time-course experiments with mathematical modelling to focus on the common situation in which variations between profiles of experimental time series, from different cell types, are observed. To understand how biochemical reactions are causing the observed variations, we performed a parameter identifiability analysis. We successfully identified reactions that differ in pancreatic stellate cells and cancer cells, by comparing confidence intervals of parameter value estimates and the variability of model trajectories. Our analysis shows that useful information can also be obtained from nonidentifiable parameters. For the prediction of potential therapeutic targets we studied the consequences of uncertainty in the values of identifiable and nonidentifiable parameters. Interestingly, the sensitivity of model variables is robust against parameter variations and against differences between IFNγ induced STAT1 signalling in pancreatic stellate and cancer cells. This provides the basis for a prediction of therapeutic targets that are valid for both cell types.

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: 23284277 Submission Date: 24 Sep 2015 12:05:46 UTC Last Modification Date: 16 Feb 2016 12:13:38 UTC Creation Date: 14 Mar 2015 08:22:46 UTC
Mathematical expressions
Rules
Assignment Rule (variable: STAT1c (observed)) Assignment Rule (variable: STAT1 (observed)) Assignment Rule (variable: RSNC (observed)) Assignment Rule (variable: SOCS1 (observed))
Assignment Rule (variable: STAT1Dn (observed)) Assignment Rule (variable: STAT1Dc (observed)) Assignment Rule (variable: STAT1D (observed)) Assignment Rule (variable: STAT1n (observed))
Rate Rule (variable: Ifng) Rate Rule (variable: II) Rate Rule (variable: d1) Rate Rule (variable: d2)
Rate Rule (variable: d3) Rate Rule (variable: d4) Rate Rule (variable: STAT1D) Rate Rule (variable: STAT1Dn)
Rate Rule (variable: i1) Rate Rule (variable: i2) Rate Rule (variable: i3) Rate Rule (variable: i4)
Rate Rule (variable: j1) Rate Rule (variable: j2) Rate Rule (variable: j3) Rate Rule (variable: j4)
Rate Rule (variable: Ir) Rate Rule (variable: STAT1Uc) Rate Rule (variable: STAT1Un) Rate Rule (variable: SOCS1)
Physical entities
Compartments Species
cell Ifng II d1
d2 d3 d4
STAT1D STAT1Dn i1
i2 i3 i4
j1 j2 j3
j4 Ir STAT1Uc
STAT1Un SOCS1 STAT1 (observed)
STAT1c (observed) STAT1n (observed) STAT1D (observed)
STAT1Dc (observed) STAT1Dn (observed) SOCS1 (observed)
RSNC (observed)    
Global parameters
k1 k3 k4 k5
k6 k7 k8 k9
k10 k11 k12 tau_1
tau_2 tau_3 WB_STAT1 WB_STAT1c
WB_STAT1n WB_STAT1D WB_STAT1Dc WB_STAT1Dn
PCR_SOCS1 scale_RSNCex    
Reactions (0)
Rules (28)
 
 Assignment Rule (name: Stat1cex) STAT1c (observed) = (Stat1U+Stat1Pd)*scale_Stat1cex
 
 Assignment Rule (name: Stat1ex) STAT1 (observed) = (Stat1U+Stat1Pd+Stat1Un+Stat1Pdn)/2
 
 Assignment Rule (name: RSNCex) RSNC (observed) = (Stat1Un+Stat1Pdn)/(Stat1U+Stat1Pd)
 
 Assignment Rule (name: Socs1ex) SOCS1 (observed) = mRNA
 
 Assignment Rule (name: Stat1Pnex) STAT1Dn (observed) = Stat1Pdn*scale_Stat1Pnex
 
 Assignment Rule (name: Stat1Pcex) STAT1Dc (observed) = Stat1Pd*scale_Stat1Pcex
 
 Assignment Rule (name: Stat1Pex) STAT1D (observed) = (Stat1Pd+Stat1Pdn)/2*scale_Stat1Pex
 
 Assignment Rule (name: Stat1nex) STAT1n (observed) = (Stat1Un+Stat1Pdn)*scale_Stat1nex
 
 Rate Rule (name: Ifng) d [ Ifng] / d t= -k1*Ifng*Ir
 
 Rate Rule (name: II) d [ II] / d t= k1*Ifng*Ir
 
 Rate Rule (name: d1) d [ d1] / d t= 4*(II-d1)/taud
 
 Rate Rule (name: d2) d [ d2] / d t= 4*(d1-d2)/taud
 
 Rate Rule (name: d3) d [ d3] / d t= 4*(d2-d3)/taud
 
 Rate Rule (name: d4) d [ d4] / d t= 4*(d3-d4)/taud
 
 Rate Rule (name: Stat1Pd) d [ STAT1D] / d t= k4*II*Stat1U/(1+k14*j4)-k6*Stat1Pd
 
 Rate Rule (name: Stat1Pdn) d [ STAT1Dn] / d t= k6*Stat1Pd-k5*Stat1Pdn
 
 Rate Rule (name: i1) d [ i1] / d t= 4*(Stat1Pdn-i1)/tau
 
 Rate Rule (name: i2) d [ i2] / d t= 4*(i1-i2)/tau
 
 Rate Rule (name: i3) d [ i3] / d t= 4*(i2-i3)/tau
 
 Rate Rule (name: i4) d [ i4] / d t= 4*(i3-i4)/tau
 
 Rate Rule (name: j1) d [ j1] / d t= 4*(mRNA-j1)/tauj
 
 Rate Rule (name: j2) d [ j2] / d t= 4*(j1-j2)/tauj
 
 Rate Rule (name: j3) d [ j3] / d t= 4*(j2-j3)/tauj
 
 Rate Rule (name: j4) d [ j4] / d t= 4*(j3-j4)/tauj
 
 Rate Rule (name: Ir) d [ Ir] / d t= -k1*Ifng*Ir
 
 Rate Rule (name: Stat1U) d [ STAT1Uc] / d t= k3*d4+k12*Stat1Un-k11*Stat1U-k4*II*Stat1U/(1+k14*j4)
 
 Rate Rule (name: Stat1Un) d [ STAT1Un] / d t= k11*Stat1U-k12*Stat1Un+k5*Stat1Pdn
 
 Rate Rule (name: mRNA) d [ SOCS1] / d t= k13+k9*i4-k10*mRNA
 
   cell Spatial dimensions: 3.0  Compartment size: 1.0
 
 Ifng
Compartment: cell
Initial concentration: 100.0
 
   II
Compartment: cell
Initial concentration: 0.0
 
   d1
Compartment: cell
Initial concentration: 0.0
 
   d2
Compartment: cell
Initial concentration: 0.0
 
   d3
Compartment: cell
Initial concentration: 0.0
 
   d4
Compartment: cell
Initial concentration: 0.0
 
 STAT1D
Compartment: cell
Initial concentration: 0.0
 
 STAT1Dn
Compartment: cell
Initial concentration: 0.0
 
   i1
Compartment: cell
Initial concentration: 0.0
 
   i2
Compartment: cell
Initial concentration: 0.0
 
   i3
Compartment: cell
Initial concentration: 0.0
 
   i4
Compartment: cell
Initial concentration: 0.0
 
   j1
Compartment: cell
Initial concentration: 0.0
 
   j2
Compartment: cell
Initial concentration: 0.0
 
   j3
Compartment: cell
Initial concentration: 0.0
 
   j4
Compartment: cell
Initial concentration: 0.0
 
   Ir
Compartment: cell
Initial concentration: 0.05721
 
 STAT1Uc
Compartment: cell
Initial concentration: 0.950418
 
 STAT1Un
Compartment: cell
Initial concentration: 0.661213
 
 SOCS1
Compartment: cell
Initial concentration: 0.108325
 
  STAT1 (observed)
Compartment: cell
Initial concentration: 0.8058155
 
  STAT1c (observed)
Compartment: cell
Initial concentration: 0.710624687346
 
  STAT1n (observed)
Compartment: cell
Initial concentration: 0.80569465263
 
  STAT1D (observed)
Compartment: cell
Initial concentration: 0.0
 
  STAT1Dc (observed)
Compartment: cell
Initial concentration: 0.0
 
  STAT1Dn (observed)
Compartment: cell
Initial concentration: 0.0
 
  SOCS1 (observed)
Compartment: cell
Initial concentration: 0.108325
 
  RSNC (observed)
Compartment: cell
Initial concentration: 0.69570757287846
 
Global Parameters (22)
 
 k1
Value: 9.4915E-4
Constant
 
 k3
Value: 0.0959796
Constant
 
 k4
Value: 0.0997621
Constant
 
 k5
Value: 298.763
Constant
 
 k6
Value: 0.0666851
Constant
 
 k7
Value: 4179.56
Constant
 
   k8
Value: 0.0583427
Constant
 
   k9
Value: 8.90244
Constant
 
   k10
Value: 12.2679
Constant
 
   k11
Value: 0.00949819
Constant
 
   k12
Value: 0.748449
Constant
 
   tau_1
Value: 277.363
Constant
 
   tau_2
Value: 79.3354
Constant
 
   tau_3
Value: 451.937
Constant
 
 WB_STAT1
Value: 1.0
Constant
 
 WB_STAT1c
Value: 0.747697
Constant
 
   WB_STAT1n
Value: 1.21851
Constant
 
   WB_STAT1D
Value: 34.4009
Constant
 
   WB_STAT1Dc
Value: 19.0574
Constant
 
   WB_STAT1Dn
Value: 91677.7
Constant
 
   PCR_SOCS1
Value: 1.0
Constant
 
   scale_RSNCex
Value: 1.0
Constant
 
Representative curation result(s)
Representative curation result(s) of BIOMD0000000585

Curator's comment: (updated: 12 Feb 2016 16:53:21 GMT)

There are four variants of this model, basically for two different cell lines with two different experimental conditions. This model correspond to the Interferon-gamma induced STAT1 signalling in the pancreatic cancer cell (IFNgamma=100ng/ml). The first row of figure S2 in the supplementary material of the paper is reproduced here. The model was integrated using Copasi v4.15 (Build 95) and the plots were generated using gnuplot.
Note: In addition, the four variants of the model and its associated SEDML files can obtained from the link below.
Note: The parameters k_v, k_0 and k_2 are omitted from the original model as k_v has the value 1 and k_0 and k_2 have the value 0, which makes them redundant.

Additional file(s)
  • Model variants (two different cell lines and and different experimental conditions):
    There are four variants of this model, basically for two different cell lines with two different experimental conditions. The COMBINE archive file has the SBML file with details explained about each model, and the details can also be found in the README file.
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