Genome-wide analysis of the role of S276 phosphorylation of p65-NFkappaB on the regulation of gene expression in response to EGF or IL-1 treatment.
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE73914 | GEO | 2018/08/31
REPOSITORIES: GEO
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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).
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