Computational model for effects of ligand/receptor binding properties on interleukin-2 trafficking dynamics and T cell proliferation response
Catherine
Lloyd
Auckland Bioengineering Institute, The University of Auckland
Model Status
The model is known to run in PCEnv, OpenCell and COR and the results replicate the published paper (Figure 5), as generated by the original model. The units have been checked and are consistent.
Model Structure
ABSTRACT: Multisubunit cytokine receptors such as the heterotrimeric receptor for interleukin-2 (IL-2) are ubiquitous in hematopoeitic cell types of importance in biotechnology and are crucial regulators of cell proliferation and differentiation behavior. Dynamics of cytokine/receptor endocytic trafficking can significantly impact cell responses through effects of receptor down-regulation and ligand depletion, and in turn are governed by ligand/receptor binding properties. We describe here a computational model for trafficking dynamics of the IL-2 receptor (IL-2R) system, which is able to predict T cell proliferation responses to IL-2. This model comprises kinetic equations describing binding, internalization, and postendocytic sorting of IL-2 and IL-2R, including an experimentally derived dependence of cell proliferation rate on these properties. Computational results from this model predict that IL-2 depletion can be reduced by decreasing its binding affinity for the IL-2R betagamma subunit relative to the alpha subunit at endosomal pH, as a result of enhanced ligand sorting to recycling vis-a-vis degradation, and that an IL-2 analogue with such altered binding properties should exhibit increased potency for stimulating the T cell proliferation response. These results are in agreement with our recent experimental findings for the IL-2 analogue termed 2D1 [Fallon, E. M. et al. J. Biol. Chem. 2000, 275, 6790-6797]. Thus, this type of model may enable prediction of beneficial cytokine/receptor binding properties to aid development of molecular design criteria for improvements in applications such as in vivo cytokine therapies and in vitro hematopoietic cell bioreactors.
The original paper reference is cited below:
Computational Model for Effects of Ligand/Receptor Binding Properties on Interleukin-2 Trafficking Dynamics and T Cell Proliferation Response, Eric M. Fallon and Douglas A. Lauffenburger, 2000, Biotechnology Progress, 16, 905-916. PubMed ID: 11027188
diagram of the ligand-receptor binding model
A schematic diagram of Fallon and Lauffenburger's 2000 computational model of IL-2-receptor binding and trafficking. The model follows the path of an extracellular ligand as it is bound to a cell surface receptor, internalised, degraded or recycled.
$\frac{d \mathrm{Rs}}{d \mathrm{time}}=(\mathrm{kr}+\mathrm{ksyn})\mathrm{Cs}+\mathrm{Vs}-\mathrm{kf}L\mathrm{Rs}+\mathrm{kt}\mathrm{Rs}$
$\frac{d \mathrm{Cs}}{d \mathrm{time}}=\mathrm{kf}L\mathrm{Rs}-(\mathrm{kr}+\mathrm{ke})\mathrm{Cs}$
$\frac{d \mathrm{Ri}}{d \mathrm{time}}=\mathrm{kre}\mathrm{Ci}+\mathrm{kt}\mathrm{Rs}-\mathrm{kfe}\mathrm{Li}\mathrm{Ri}+\mathrm{kh}\mathrm{Ri}$
$\frac{d \mathrm{Ci}}{d \mathrm{time}}=\mathrm{kfe}\mathrm{Li}\mathrm{Ri}+\mathrm{ke}\mathrm{Cs}-(\mathrm{kre}+\mathrm{kh})\mathrm{Ci}$
$\frac{d \mathrm{Li}}{d \mathrm{time}}=\frac{\mathrm{kre}\mathrm{Ci}-\mathrm{kfe}\mathrm{Li}\mathrm{Ri}}{\mathrm{Ve}\mathrm{NA}}-\mathrm{kx}\mathrm{Li}$
$\frac{d \mathrm{Ld}}{d \mathrm{time}}=\mathrm{kh}\mathrm{Ci}$
$\frac{d L}{d \mathrm{time}}=\frac{(\mathrm{kr}\mathrm{Cs}+\mathrm{kx}\mathrm{Li}\mathrm{Ve}\mathrm{NA}-\mathrm{kf}L\mathrm{Rs})Y}{\mathrm{NA}}$
$\frac{d Y}{d \mathrm{time}}=\begin{cases}(\frac{600\mathrm{Cs}}{250+\mathrm{Cs}}-200)\times 1E3 & \text{if $\frac{600\mathrm{Cs}}{250+\mathrm{Cs}}-200> 0$}\\ 0 & \text{otherwise}\end{cases}$
$\mathrm{kf}=\begin{cases}\frac{\mathrm{kr}}{11.1} & \text{if $\mathrm{IL2}=1$}\\ \frac{\mathrm{kr}}{8.2} & \text{otherwise}\end{cases}\mathrm{kre}=\begin{cases}\mathrm{kr}\times 8 & \text{if $\mathrm{IL2}=1$}\\ \mathrm{kr}\times 5 & \text{otherwise}\end{cases}\mathrm{kfe}=\begin{cases}\frac{\mathrm{kre}}{1000} & \text{if $\mathrm{IL2}=1$}\\ \frac{\mathrm{kre}}{3000} & \text{otherwise}\end{cases}$
The University of AucklandThe Bioengineering Institute11027188AAutumnCuellarCatherine Lloydintracellular IL-2 receptorRiThis is the CellML description of Fallon and Lauffenburger's 2000
model of the effects of ligand-receptor binding properties on
interleukin-2 trafficking dynamics and T cell proliferation response.MayCatherineLloyd2002-10-15T00:00:00+00:00Recoded the model to remove the reaction element. The model now runs in PCEnv and COR to generate results which are close to those in the original published paper.t-cellinterleukin-2pharmacodynamicsimmunologyIL-2signal transductionT cellil-21008000ADouglasLauffenburger2003-04-09keywordCatherine LloydMayCatherineLloydintracellular ligand-receptor complexCicell surface ligand-receptor complexCsintracellular IL-2 ligandLi
Added publication date information.
Fallon and Lauffenburger's 2000 model of the effects of ligand-receptor
binding properties on interleukin-2 trafficking dynamics and T cell
proliferation response.
c.lloyd@auckland.ac.nzThis CellML model has been recoded to remove the reaction element. The model is known to run in PCEnv and COR and the results are close to those in the published paper which are generated by the original model. The units have been checked and are consistent. We suspect this model might generate the exact published results if the original initial conditions were used. As it is, the paper does not state all of these values, but instead it explains that these values are calculated throughout the simulation and subsequently fed back into the model.extracellular IL-2 ligandLdegraded IL-2 ligandLdThe University of Auckland, Bioengineering Institute2000-0991616905Computational Model for Effects of Ligand/Receptor Binding
Properties on Interleukin-2 Trafficking Dynamics and T Cell
Proliferation Responsecell surface IL-2 receptorRsBiotechnology ProgressMEricFalloncell densityY2008-01-01T14:41:04+13:00