Signal processing in the TGF-beta superfamily ligand-receptor network (Standard Form)

Signal processing in the TGF-beta superfamily ligand-receptor network

Model Status

This CellML model has been checked in both PCEnv and COR and the model runs in both to recreate figure 5A in the paper. This particular version of the model describes a two-compartment model of receptor trafficking for one ligand. The equations have been written to look like those in figure 4 of the paper. Initial conditions were not provided in the paper so were taken from the SBML model in the BioModels database (BIOMD0000000101). Also note that the model only runs correctly if time in minutes is defined as 3600s, and the ligand concentration is increased from 3E-5 to 0.01 at time t=2500 to ensure that the system reaches steady state. Hence, the time t=0 of the paper corresponds to t=2500 in the model (alo in accordance with the SBML model).

Model Structure

ABSTRACT: The TGF-beta pathway plays a central role in tissue homeostasis and morphogenesis. It transduces a variety of extracellular signals into intracellular transcriptional responses that control a plethora of cellular processes, including cell growth, apoptosis, and differentiation. We use computational modeling to show that coupling of signaling with receptor trafficking results in a highly versatile signal-processing unit, able to sense by itself absolute levels of ligand, temporal changes in ligand dimensionless, and ratios of multiple ligands. This coupling controls whether the response of the receptor module is transient or permanent and whether or not different signaling channels behave independently of each other. Our computational approach unifies seemingly disparate experimental observations and suggests specific changes in receptor trafficking patterns that can lead to phenotypes that favor tumor progression.

Two-Compartment Model of Receptor Trafficking and Signaling.

The original paper reference is cited below:

Signal processing in the TGF-beta superfamily ligand-receptor network, Jose M. G. Vilar, Ronald Jansen and Chris Sander, 2006, PLoS Computational Biology, volume 2, 36-45. PubMed ID: 16446785