Supplementary Materials

Supplementary Materials for:

Context-specific flow through the MEK/ERK module produces cell- and ligand-specific patterns of ERK single and double phosphorylation

Nao Iwamoto, Lorenza A. D'Alessandro, Sofia Depner, Bettina Hahn, Bernhard A. Kramer, Philippe Lucarelli, Artyom Vlasov, Markus Stepath, Martin E. Böhm, Daniela Deharde, Georg Damm, Daniel Seehofer, Wolf D. Lehmann, Ursula Klingmüller, Marcel Schilling*

*Corresponding author. E-mail: m.schilling{at}dkfz.de

This PDF file includes:

  • Text S1. Quantitative dynamic modeling of the MEK/ERK module.
  • Text S2. Estimation of data noise of mass spectrometry data by data-based quantitative dynamic modeling.
  • Fig. S1. Quantitative immunoblotting of MEK and one-dimensional SDS-PAGE of ERK.
  • Fig. S2. Stoichiometric ratio of ERK1 and ERK2.
  • Fig. S3. Model-based estimation of experimental error.
  • Fig. S4. Data-based model calibration with the comprehensive ERK model.
  • Fig. S5. Data-based model calibration with the processive ERK model.
  • Fig. S6. Data-based model calibration with the reduced HaCaT model without feedbacks.
  • Fig. S7. Data-based model calibration with the final ERK model.
  • Fig. S8. Data-based model calibration with a modified PMH model with a dynamic feedback allowing ERK phosphorylation via pY-ERK1/2.
  • Fig. S9. Validation of the identifiable model structures by quantitative dynamic modeling.
  • Fig. S10. Screening of potential candidates of the negative and positive feedback loops in HaCaT cells.
  • Fig. S11. Distribution profiles of the phosphorylated forms of ERK after parameter variance.
  • Fig. S12. Results of model simulation after changing the rate constant of pT-ERK1/2 dephosphorylation.
  • Fig. S13. Histology of patient samples.
  • Table S1. MEK model: variables, input variables, additional variables, parameters, reactions, start value assignments, additional parameters, observed variables, and ODEs.
  • Table S2. Error model: variables, dynamic parameters, error parameters, reactions, observed variables, error structure, and ODEs.
  • Table S3. ERK model: variables, additional variables, parameters, reactions, observed variables, and ODEs.
  • Table S4. Parameter identifiability.
  • Table S5. Negative feedback: additional observed variables and additional parameters.
  • Table S6. Goodness of fit of model calibration of DUSP4 and DUSP6 data.
  • Table S7. Positive feedback: additional observed variables and additional parameters.
  • Table S8. Model prediction HaCaT cells: input variables, parameters, and reactions.
  • Table S9. Model prediction PMH: variables, parameters, reactions, and ODEs.
  • Table S10.Primers used in qRT-PCR experiments in PMHs and HaCaT cells and gene names.
  • Legends for data files S1 to S4

[Download PDF]

Technical Details

Format: Adobe Acrobat PDF

Size: 6.42 MB

Other Supplementary Material for this manuscript includes the following:

  • Data file S1 (.xml format). SBML file of the PMH model.
  • Data file S2 (.txt format). Start value assignment for the PMH model.
  • Data file S3 (.xml format). SBML file of the HaCaT model.
  • Data file S4 (.txt format). Start value assignment for the HaCaT model.

[Download Data files S1 to S4]


Citation: N. Iwamoto, L. A. D'Alessandro, S. Depner, B. Hahn, B. A. Kramer, P. Lucarelli, A. Vlasov, M. Stepath, M. E. Böhm, D. Deharde, G. Damm, D. Seehofer, W. D. Lehmann, U. Klingmüller, M. Schilling, Context-specific flow through the MEK/ERK module produces cell- and ligand-specific patterns of ERK single and double phosphorylation. Sci. Signal. 9, ra13 (2016).

© 2016 American Association for the Advancement of Science