Dataset Information

Nakakuki2010_CellFateDecision_Core

ABSTRACT: 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.

SUBMITTER: Lutz Brusch

PROVIDER: BIOMD0000000251 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000251?filename=BIOMD0000000251-biopax2.owl	Owl
	BIOMD0000000251?filename=BIOMD0000000251-biopax3.owl	Owl
	BIOMD0000000251?filename=BIOMD0000000251-matlab.m	Other
	BIOMD0000000251?filename=BIOMD0000000251-octave.m	Other
	BIOMD0000000251?filename=BIOMD0000000251.m	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

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Project description: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

Project description:Germanos M1*. Yau B1*. Taper M1. An Y1. Yeoman C1. Wilson A1. Cattin-Ortolá J2. Maslar D3. Tong J1. Naghiloo S1. Needham E4. Diaz-Vegas A4. Larance M1. Thomas H5. Seidah NG6. von Blume J7. James DE4. Thorn P1. Ailion M2. Asensio C3. Kebede MA1. 1 School of Medical Sciences, Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia. 2 Department of Biochemistry, University of Washington, Seattle, WA USA. 3 Department of Biological Sciences, University of Denver, Denver, Colorado 80210, United States. 4 Discipline of Biochemistry and Molecular Biology, School of Life and Environmental Sciences, Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia. 5 Department of Medicine, St Vincent's Hospital, University of Melbourne, Fitzroy, Melbourne, Victoria, Australia. 6 Laboratory of Biochemical Neuroendocrinology, Institut de Recherches Cliniques de Montréal, Montreal, QC, Canada. 7 Department of Cell Biology, Yale University School of Medicine, New Haven, CT 06510.. *These authors contributed equally. Abstract Here we investigate the role of Cab45 in β-cell function and insulin secretory granule (ISG) biogenesis. Cab45 has been known as a ubiquitously expressed, soluble, trans-Golgi network (TGN) resident protein. We demonstrate its expression in pancreatic islets but not exocrine tissue, and localization in both Golgi and granular/vesicular compartments of primary β-cells. In INS1 cells, Cab45 is held in the cis-Golgi by a calcium-dependent mechanism but is capable of anterograde trafficking beyond the TGN. ISG biogenesis is maintained in Cab45 knockout INS1 cells which exhibit insulin hypersecretion due to dysregulated calcium homeostasis, leading to ISG depletion that is reinstated by rest at low glucose or Cab45 overexpression. Overexpressed Cab45 traffics directly into ISGs and increases biogenesis. Expression of a Cab45 variant that cannot bind calcium drives superior ISG rescue and has reduced ISG residency. In humans, anterograde Cab45 trafficking increases with worsening obesity, suggesting recruitment to drive cargo transit and support secretory output. Humans with type 2 diabetes, however, contain a greater proportion of Cab45-occupied ISGs and a loss of Golgi-localized Cab45 compared to non-diabetics. These insights position Cab45 as an exciting candidate for further investigation into mechanisms associated with β-cell compensation and failure.