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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 | 2024-09-02

REPOSITORIES: BioModels

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	BIOMD0000000033?filename=BIOMD0000000033-biopax2.owl	Owl
	BIOMD0000000033?filename=BIOMD0000000033-biopax3.owl	Owl
	BIOMD0000000033?filename=BIOMD0000000033-matlab.m	Other
	BIOMD0000000033?filename=BIOMD0000000033-octave.m	Other
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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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