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Brown2004 - NGF and EGF signaling

ABSTRACT: Brown2004 - NGF and EGF signaling This model is described in the article: The statistical mechanics of complex signaling networks: nerve growth factor signaling. Brown KS, Hill CC, Calero GA, Myers CR, Lee KH, Sethna JP, Cerione RA. Phys Biol 2004 Dec; 1(3-4): 184-195 Abstract: The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while significant effort is being made to experimentally measure the rate constants for individual steps in these networks, many of the parameters required to describe their behavior remain unknown or at best represent estimates. To establish the usefulness of our approach, we have applied our methods toward modeling the nerve growth factor (NGF)-induced differentiation of neuronal cells. In particular, we study the actions of NGF and mitogenic epidermal growth factor (EGF) in rat pheochromocytoma (PC12) cells. Through a network of intermediate signaling proteins, each of these growth factors stimulates extracellular regulated kinase (Erk) phosphorylation with distinct dynamical profiles. Using our modeling approach, we are able to predict the influence of specific signaling modules in determining the integrated cellular response to the two growth factors. Our methods also raise some interesting insights into the design and possible evolution of cellular systems, highlighting an inherent property of these systems that we call 'sloppiness.' The figures in the paper show results from computations performed over an ensemble of all parameter sets that fit the available data. This file contains only the best fit parameters. The full ensemble of parameters is available at http://www.lassp.cornell.edu/sethna/GeneDynamics/PC12DataFiles/ (Also, the best-fit parameter set produces a curve for DN Rap1 that is less "peakish" than the ensemble average.) The conversion factors for EGF and NGF concentrations account for their molecular weights and the density of cells in the culture dish. These concentrations are saturating, so the exact values are not critical. Because the Erk data fit to measure only fold changes in activity, there is no absolute scale for the y-axes. Thus the curves from this file have different magnitudes than those published. To reproduce the figures from the paper: 2a) For EGF stimulation, set the initial concentration of EGF to 100 ng/ml * 100020 (molecule/cell)/(ng/ml) = 10002000. For NGF stimulation, set the initial concentration of NGF to 50 ng/ml * 4560 (molecule/cell)/(ng/ml) = 456000 5a) To simulate LY294002 addition, set kPI3KRas and kPI3K to 0. 5b) To simulate a dominant negative Rap1, set kRap1ToBRaf to 0. To simulate a dominant negative Ras, set kRasToRaf1 and kPI3KRas to 0. Almost all the data fit with this model by the authors are from Western blots. Given the uncertainties in antibody effectiveness and other factors, one can't a priori derive a conversion between the arbitrary units for a given set of data and molecules per cell. So the authors used an adjustable "scale factor" that converts between molecules per cell and Western blot units. For the EGF stimulation data in figure 2a) the scale factor conversion is 1.414e-05 (U/mg)/(molecule/cell). For the NGF stimulation data in figure 2a) it is 7.135e-06 (U/mg)/(molecule/cell). This model is hosted on BioModels Database and identified by: BIOMD0000000033. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. 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.

SUBMITTER: Ryan Gutenkunst

PROVIDER: BIOMD0000000033 | BioModels | 2005-06-10

REPOSITORIES: BioModels

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The statistical mechanics of complex signaling networks: nerve growth factor signaling.

Brown K S KS Hill C C CC Calero G A GA Myers C R CR Lee K H KH Sethna J P JP Cerione R A RA

Physical biology 20041201 3-4

The inherent complexity of cellular signaling networks and their importance to a wide range of cellular functions necessitates the development of modeling methods that can be applied toward making predictions and highlighting the appropriate experiments to test our understanding of how these systems are designed and function. We use methods of statistical mechanics to extract useful predictions for complex cellular signaling networks. A key difficulty with signaling models is that, while signifi ...[more]

PMID: 16204838

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Project description:This model is from the article: Quantitative analysis of transient and sustained transforming growth factor-β signaling dynamics. Zhike Zi, Zipei Feng, Douglas A Chapnick, Markus Dahl, Difan Deng, Edda Klipp, Aristidis Moustakas & Xuedong Liu Molecular Systems Biology 2011 May 24;7:492. 21613981 , Abstract: Mammalian cells can decode the concentration of extracellular transforming growth factor-β (TGF-β) and transduce this cue into appropriate cell fate decisions. How variable TGF-β ligand doses quantitatively control intracellular signaling dynamics and how continuous ligand doses are translated into discontinuous cellular fate decisions remain poorly understood. Using a combined experimental and mathematical modeling approach, we discovered that cells respond differently to continuous and pulsating TGF-β stimulation. The TGF-β pathway elicits a transient signaling response to a single pulse of TGF-β stimulation, whereas it is capable of integrating repeated pulses of ligand stimulation at short time interval, resulting in sustained phospho-Smad2 and transcriptional responses. Additionally, the TGF-β pathway displays different sensitivities to ligand doses at different time scales. While ligand-induced short-term Smad2 phosphorylation is graded, long-term Smad2 phosphorylation is switch-like to a small change in TGF-β levels. Correspondingly, the short-term Smad7 gene expression is graded, while long-term PAI-1 gene expression is switch-like, as is the long-term growth inhibitory response. Our results suggest that long-term switch-like signaling responses in the TGF-β pathway might be critical for cell fate determination. Note: Developer of the model: Zhike Zi Reference: Zi Z. et al., Quantitative Analysis of Transient and Sustained Transforming Growth Factor-beta Signaling Dynamics, Molecular Systems Biology, 2011 1. The global parameter that set the type of stimulation (a) for sustained TGF-beta stimulation: set stimulation_type = 1. (b) for single pulse of TGF-beta stimulation: set stimulation_type = 2. parameter "single_pulse_duration" is for the duration of stimulation, for example, single_pulse_duration = 0.5, for 0.5 min (30 seconds) of TGF-beta stimulation. *Note: make sure that the time course cover the time point when the event is triggered. (c) for single pulse of TGF-beta stimulation in COPASI change the trigger of event "single_pulse_TGF_beta_washout" from "and(eq(stimulation_type, 2), eq(time, single_pulse_duration))" (for SBML-SAT) to "and(eq(stimulation_type, 2), gt(time, single_pulse_duration))" (for COPASI) 2. Notes for TGF-beta dose in terms of molecules per cell (a) The following equation applies for conversion of TGF-beta dose in molecules per cell TGF_beta_dose_mol_per_cell = initial TGF_beta_ex*1e-9*Vmed*6e23 (b) for standard experimental setup 1e6 cells in 2 mL medium 0.001 nM initial TGF_beta_ex is approximately equal to the dose of 1200 TGF-beta molecules/cell 0.050 nM initial TGF_beta_ex is approximately equal to the dose of 60000 TGF-beta molecules/cell (c) For 1e6 cells in 10 mL medium, please change the initial compartment size of Vmed and the corresponding assignment rule for Vmed. initial Vmed = 1e-8 (1e6 cells in 10 mL medium) Vmed = 0.010/(1e6*exp(log(1.45)*time/1440)) (1e6 cells in 10 mL medium) 3. Please note that this model contains events and the medium compartment size is varied. 4. For the model simulation in SBML-SAT, please remove initialAssignments and save it as SBML Level 2 Verion 1 file.

Project description:NGF dependent Akt pathway model made by Kazuhiro A. Fujita. This is the NGF dependent Akt pathway model described in: Decoupling of receptor and downstream signals in the Akt pathway by its low-pass filter characteristics. Fujita KA, Toyoshima Y, Uda S, Ozaki Y, Kubota H, and Kuroda S. Sci Signal. 2010 Jul 27;3(132):ra56. PMID: 20664065 ; DOI: 10.1126/scisignal.2000810 Abstract: In cellular signal transduction, the information in an external stimulus is encoded in temporal patterns in the activities of signaling molecules; for example, pulses of a stimulus may produce an increasing response or may produce pulsatile responses in the signaling molecules. Here, we show how the Akt pathway, which is involved in cell growth, specifically transmits temporal information contained in upstream signals to downstream effectors. We modeled the epidermal growth factor (EGF)–dependent Akt pathway in PC12 cells on the basis of experimental results. We obtained counterintuitive results indicating that the sizes of the peak amplitudes of receptor and downstream effector phosphorylation were decoupled; weak, sustained EGF receptor (EGFR) phosphorylation, rather than strong, transient phosphorylation, strongly induced phosphorylation of the ribosomal protein S6, a molecule downstream of Akt. Using frequency response analysis, we found that a three-component Akt pathway exhibited the property of a low-pass filter and that this property could explain decoupling of the peak amplitudes of receptor phosphorylation and that of downstream effectors. Furthermore, we found that lapatinib, an EGFR inhibitor used as an anticancer drug, converted strong, transient Akt phosphorylation into weak, sustained Akt phosphorylation, and, because of the low-pass filter characteristics of the Akt pathway, this led to stronger S6 phosphorylation than occurred in the absence of the inhibitor. Thus, an EGFR inhibitor can potentially act as a downstream activator of some effectors. The different versions of input, step, pulse and ramp, can be simulated using the parameters NGF_conc_pulse , NGF_conc_step and NGF_conc_ramp . Depending on which one is set unequal to 0, either a continous pulse with value NGF_conc_pulse , a 60 second step with NGF_conc_step or a signal increasing from 0 to NGF_conc_pulse over a time periode of 3600 seconds are used as input. In case more than one parameter is set to values greater than 0 these input profiles are added to each other. The pulse time and the time over which the ramp input increases can be set by pulse_time and ramp_time . This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team. For more information see the terms of use . To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

Dataset Information

Brown2004 - NGF and EGF signaling

Publications

The statistical mechanics of complex signaling networks: nerve growth factor signaling.

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