Jörg Schaber et al.
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modelMaGe
Automated ensemble modeling with modelMaGe:
analyzing feedback mechanisms in the Sho1 branch
of the HOG pathway
Supporting Information
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Jörg Schaber1,2*, Max Flöttmann2, Jian Li2, Carl-Fredrik Tiger3, Stefan Hohmann3,
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Edda Klipp2*
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Institute for Experimental Internal Medicine, Medical Faculty, Otto von Guericke
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University, Magdeburg, Germany
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Germany
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Sweden
Theoretical Biophysics, Department of Biology, Humboldt University, Berlin,
Department of Cell and Molecular Biology, University of Gothenburg, Göteborg,
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To whom correspondence should be addressed:
Jörg Schaber
Institute of Experimental Internal Medicine
Medical Faculty
Otto von Guericke University
Leipziger Str. 44, 39120 Magdeburg, Germany
Phone: +49 391 67 14453
Fax: +49 391 67 13312
Email: [email protected]
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modelMaGe
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Model generation
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The set of candidate models are automatically generated, fitted and discriminated by
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modelMaGe. Having modelMaGe and its prerequisite software installed as described
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in the documentation at www.modelmage.org, this achieved by a single simple
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command:
modelmage.py –p –i Sho1.ini Sho1Master.cps
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The –p option indicates that the generated models shall be fitted according to the
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parameter estimation task that is defined in the master model Sho1Master.cps.
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The file Sho1.ini specifies how the candidate models are generated from the
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master model by leaving out components as species, reactions and modifiers and by
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inserting alternative kinetics for certain reactions.
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Both the master model Sho1Master.cps as well the specifications file
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Sho1.ini can be downloaded from the journal’s website.
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The models
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In the following, we provide diagrams of all generated candidate models. The models
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are displayed as bipartide graphs. Arrows indicate substrates and products of
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reactions, end-dotted lines indicate modifiers of reactions. MM-tags and MA-tags at
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the reactions indicate whether Michaelis-Menten kinetic or mass-action kinetic is
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used, respectively. Note that in the model diagrams the names of the components are
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displayed, whereas the generation directives operate on identifiers, which are defined
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in the corresponding SBML file. A mapping of names and identifiers can be displayed
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with modelMaGe with the –s option.
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Figure S1: The master model, including all components (ovals) and reactions
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(rectangles) of the potential candidate models. The model is depicted as a bipartide
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graph. Species and reactions are named as in the corresponding Copasi file. This is the
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data structure modelMaGe uses for model generation. Arrows indicate substrates and
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products of reactions, end-dotted lines indicate modifiers of reaction. Light gray
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indicate components of the original model C10 by Hao et al. (2007) (Table 1). Dark
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gray components indicate components of the C5c model (Table 1). Hatched
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components are part of both models.
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modelMaGe
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Figure S2: The C10 model, originally published by Hao et al. (2007) (Model IIIa).
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The model is generated from the master model by the command: modelmage.py -
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r ‘species_11 & species_12 & reaction_9:species_3’ –k
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‘reaction_1(mMM) reaction_9(mMM)‘ Sho1Master.cps
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modelMaGe
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Figure S3: The C6a model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & species_11 & species_12’ –k
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‘reaction_1(mMM) reaction_9(mMM)’ Sho1Master.cps
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Figure S4: The C6b model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & species_11 & reaction_11 & reaction_12 &
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reaction_13’-k ‘reaction_2(MA) reaction_3(mMA)
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reaction_4(MA) reaction_10(MA)’ Sho1Master.cps
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Figure S5: The C8a model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_1:species_1 &
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reaction_9:species_11’-k ‘reaction_2(MA) reaction_3(mMA)
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reaction_4(MA) reaction_10(MA)’ Sho1Master.cps
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modelMaGe
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Figure S6: The C8b model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_1:species_1 &
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reaction_9:species_11 & reaction_13:species_11 &
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reaction_12’-k ‘reaction_2(MA) reaction_3(mMA)
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reaction_4(MA) reaction_10(MA)’ Sho1Master.cps
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Figure S7: The C8c model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_1:species_1 &
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reaction_9:species_11 & reaction_12 & reaction_13’-k
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‘reaction_2(MA) reaction_3(mMA) reaction_4(MA)
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reaction_10(MA)’ Sho1Master.cps
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Figure S8: The C7a model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_3 & reaction_4 &
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reaction_1:species_1 & reaction_9:species_11’ –k
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‘reaction_2(MA) reaction_10(MA)’ Sho1Master.cps
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Figure S9: The C7b model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_3 & reaction_4 &
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reaction_1:species_1 & reaction_9:species_11 &
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reaction_13:species_11 & reaction_12’ –k ‘reaction_2(MA)
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reaction_10(MA)’ Sho1Master.cps
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Figure S10: The C7c model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_5 & species_6 & species_7
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& species_8 & reaction_3 & reaction_4 &
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reaction_1:species_1 & reaction_9:species_11 &
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reaction_12 & reaction_13’ –k ‘reaction_2(MA)
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reaction_10(MA)’ Sho1Master.cps
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Figure S11: The C5a model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_2 & species_3 & species_4
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& species_5 & species_6 & species_7 & species_8’ –k
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‘reaction_10(MA)’ Sho1Master.cps
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Figure S12: The C5b model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_2 & species_3 & species_4
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& species_5 & species_6 & species_7 & species_8 &
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reaction_13:species_11 & reaction_12’ –k
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‘reaction_10(MA)’ Sho1Master.cps
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Figure S13: The C5c model. The model is generated from the master model by the
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command: modelmage.py -r ‘species_2 & species_3 & species_4
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& species_5 & species_6 & species_7 & species_8 &
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reaction_12 & reaction_13’ –k ‘reaction_10(MA)’
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Sho1Master.cps
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Figure S14: Western Blots of the triple shock experiments (t=0, t=30min, t=60min,
0.4 M KCl each). Pictures of the three different experimental data sets (taken from the
same membrane). The phospho Thr/Tyr Hog1 signal (Hog1(TpYp) and the total Hog1
protein signal (Hog1) is shown. Lanes are named according to sample times in
minutes. (L) are lanes used for protein size ladder (the 50 kD band is visible on the
total Hog1 pictures).
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