PanRTK model for single cell line

Hass2017-PanRTK model for single cell
line
The model structure comprises
heterodimerization and receptor trafficking as described in detail
in the article below. For ligand input, set a respective
event. The illustrated event sets the EGF concentration to 2.5 nMol
in the model file.

This model is described in the article:

Predicting ligand-dependent
tumors from multi-dimensional signaling features.

Hass H, Masson K, Wohlgemuth S,
Paragas V, Allen JE, Sevecka M, Pace E, Timmer J, Stelling J,
MacBeath G, Schoeberl B, Raue A.
NPJ Syst Biol Appl 2017; 3: 27
Abstract:

Targeted therapies have shown significant patient benefit in
about 5-10% of solid tumors that are addicted to a single
oncogene. Here, we explore the idea of ligand addiction as a
driver of tumor growth. High ligand levels in tumors have been
shown to be associated with impaired patient survival, but
targeted therapies have not yet shown great benefit in
unselected patient populations. Using an approach of applying
Bagged Decision Trees (BDT) to high-dimensional signaling
features derived from a computational model, we can predict
ligand dependent proliferation across a set of 58 cell lines.
This mechanistic, multi-pathway model that features receptor
heterodimerization, was trained on seven cancer cell lines and
can predict signaling across two independent cell lines by
adjusting only the receptor expression levels for each cell
line. Interestingly, for patient samples the predicted tumor
growth response correlates with high growth factor expression
in the tumor microenvironment, which argues for a co-evolution
of both factors in vivo.

This model is hosted on
BioModels Database
and identified by:
MODEL1708210000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.

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.