Signal processing in the TGF-beta superfamily ligand-receptor network
Catherine
Lloyd
Bioengineering Institute, University of Auckland
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 in the format of reactions (of which there are 13) and the concentrations of the substrates are expressed as the sum of the reaction fluxes. 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.
model diagram
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
$\mathrm{v1}=\mathrm{ka}l\mathrm{RI}\mathrm{RII}$
$\mathrm{v2}=\mathrm{kcd}\mathrm{l\_RI\_RII}$
$\mathrm{v3}=\mathrm{klid}\mathrm{l\_RI\_RII}$
$\mathrm{v4}=\mathrm{ki}\mathrm{l\_RI\_RII}$
$\mathrm{v5}=\mathrm{p\_RI}$
$\mathrm{v6}=\mathrm{kcd}\mathrm{RI}$
$\mathrm{v7}=\mathrm{ki}\mathrm{RI}$
$\mathrm{v8}=\mathrm{kr}\mathrm{RI\_endo}$
$\mathrm{v9}=\mathrm{kr}\mathrm{l\_RI\_RII\_endo}$
$\mathrm{v10}=\mathrm{p\_RII}$
$\mathrm{v11}=\mathrm{kcd}\mathrm{RII}$
$\mathrm{v12}=\mathrm{ki}\mathrm{RII}$
$\mathrm{v13}=\mathrm{kr}\mathrm{RII\_endo}$
$l=\begin{cases}0.01 & \text{if $\mathrm{time}\ge 2500$}\\ 0.00003 & \text{otherwise}\end{cases}$
$\frac{d \mathrm{l\_RI\_RII}}{d \mathrm{time}}=\mathrm{v1}-\mathrm{v2}+\mathrm{v3}+\mathrm{v4}$
$\frac{d \mathrm{RI}}{d \mathrm{time}}=\mathrm{v5}+\mathrm{v8}+\mathrm{v9}-\mathrm{v1}+\mathrm{v6}+\mathrm{v7}$
$\frac{d \mathrm{RII}}{d \mathrm{time}}=\mathrm{v9}+\mathrm{v10}+\mathrm{v13}-\mathrm{v1}+\mathrm{v11}+\mathrm{v12}$
$\frac{d \mathrm{l\_RI\_RII\_endo}}{d \mathrm{time}}=\mathrm{v4}-\mathrm{v9}$
$\frac{d \mathrm{RI\_endo}}{d \mathrm{time}}=\mathrm{v7}-\mathrm{v8}$
$\frac{d \mathrm{RII\_endo}}{d \mathrm{time}}=\mathrm{v12}-\mathrm{v13}$
Signal processing in the TGF-beta superfamily ligand-receptor network (Reaction Form)
Lloyd
Catherine
May
c.lloyd@auckland@auckland.ac.nz
The University of Auckland
Auckland Bioengineering Institute
2009-09-28
The Vilar et al. 2006 Model of Signal Processing in the TGF-beta Superfamily Ligand-Receptor Network
This is the CellML description of Vilar et al.'s mathematical model of ligand-receptor binding and receptor complex internalisation in the TGF-beta superfamiliy ligand-receptor network
Catherine Lloyd
keyword
signal transduction
TGF-beta
16446785
Vilar
Jose
M
G
Jansen
Ronald
Sander
Chris
Signal processing in the TGF-beta superfamily ligand-receptor network
2006-01
PLoS Computational Biology
2
36
45