Dataset Information

Nakakuki2010_CellFateDecision_Mechanistic

ABSTRACT: This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. This mechanistic model comprises the explicit reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. In the below article, two different mechanistic models have been studied, the first one based on previously known interactions but failing to account for the experimental data and the second one including additional interactions which were discovered and confirmed by new experiments. The mechanistic model encoded here is the second one, the extended and at the time of creation most complete model of cell fate decision making in response to different doses of EGF or HRG stimulation. The encoded parameter set corresponds to 10mM HRG stimulation as shown in Fig.1 of the article. The Supplementary Methods of the article provide further parameter sets that allow simulations for different ligands and different doses. A corresponding core model is available from http://www.ebi.ac.uk/biomodels/ as MODEL1003170000. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010 , doi: 10.1016/j.cell.2010.03.054 (1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan (2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland (3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA (4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA (5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan (6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany

SUBMITTER: Lutz Brusch

PROVIDER: BIOMD0000000250 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000250?filename=BIOMD0000000250-biopax2.owl	Owl
	BIOMD0000000250?filename=BIOMD0000000250-biopax3.owl	Owl
	BIOMD0000000250?filename=BIOMD0000000250.m	Other
	BIOMD0000000250?filename=BIOMD0000000250.pdf	Pdf
	BIOMD0000000250?filename=BIOMD0000000250.png	Other

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Publications

Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.

Nakakuki Takashi T Birtwistle Marc R MR Saeki Yuko Y Yumoto Noriko N Ide Kaori K Nagashima Takeshi T Brusch Lutz L Ogunnaike Babatunde A BA Okada-Hatakeyama Mariko M Kholodenko Boris N BN

Cell 20100520 5

Activation of ErbB receptors by epidermal growth factor (EGF) or heregulin (HRG) determines distinct cell-fate decisions, although signals propagate through shared pathways. Using mathematical modeling and experimental approaches, we unravel how HRG and EGF generate distinct, all-or-none responses of the phosphorylated transcription factor c-Fos. In the cytosol, EGF induces transient and HRG induces sustained ERK activation. In the nucleus, however, ERK activity and c-fos mRNA expression are tra ...[more]

PMID: 20493519

Publication: 1/2

Similar Datasets

Project description:This model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. The model comprises lumped reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. The parameter set implemented here corresponds to that used for generating Figs. 4 B,C,D (red curves for 10nM HRG) of the below article in Cell (2010). Moreover, we found that the same model described well the dynamics in different cell types (MCF-7 and PC-12), of different ligands (EGF and HRG) and at different doses (0.1nM, 1nM, 10nM) for a unique set of parameter values (as implemented here and reported in Table SD4_1 of the article) except for four parameters characterising the input, cytoplasmic ppERK. These four parameters K1, K2, tau1 and tau2 are used in the two equations involving species x1 and x2. These two equations define a phenomenological input module to describe the ligand-, dose- and cell type-dependent dynamics of ppERKc which are not modelled in mechanistic detail here. The four parameter values can be adjusted to model a specific ligand, dose and cell type. 8 parameter sets for different experiments are given in Table SD4_2 of the article. This SBML file, however, carries just one such parameter set. We have chosen that of MCF-7 cells stimulated by 10nM of HRG. To reproduce all simulations from the article, please replace the parameter values for K1, K2, tau1, tau2 as needed. Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics. Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010, doi:10.1016/j.cell.2010.03.054 (1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan (2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland (3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA (4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA (5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan (6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 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.