An Integrated Model of Epidermal Growth Factor Receptor Trafficking and Signal Transduction

Abstract

Endocytic trafficking of many types of receptors can have profound effects on subsequent signaling events. Quantitative models of these processes, however, have usually considered trafficking and signaling independently. Here, we present an integrated model of both the trafficking and signaling pathway of the epidermal growth factor receptor (EGFR) using a probability weighted-dynamic Monte Carlo simulation. Our model consists of hundreds of distinct endocytic compartments and approximately 13,000 reactions/events that occur over a broad spatio-temporal range. By using a realistic multicompartment model, we can investigate the distribution of the receptors among cellular compartments as well as their potential signal transduction characteristics. Our new model also allows the incorporation of physiochemical aspects of ligand-receptor interactions, such as pH-dependent binding in different endosomal compartments. To determine the utility of this approach, we simulated the differential activation of the EGFR by two of its ligands, epidermal growth factor (EGF) and transforming growth factor-alpha (TGF-alpha). Our simulations predict that when EGFR is activated with TGF-alpha, receptor activation is biased toward the cell surface whereas EGF produces a signaling bias toward the endosomal compartment. Experiments confirm these predictions from our model and simulations. Our model accurately predicts the kinetics and extent of receptor downregulation induced by either EGF or TGF-alpha. Our results suggest that receptor trafficking controls the compartmental bias of signal transduction, rather than simply modulating signal magnitude. Our model provides a new approach to evaluating the complex effect of receptor trafficking on signal transduction. Importantly, the stochastic and compartmental nature of the simulation allows these models to be directly tested by high-throughput approaches, such as quantitative image analysis.

The EGFR demonstrates linear signal transmission

“…† This model has been widely used to describe the EGFR and other receptor tyrosine kinase systems such as HER2, 14,[16][17][18][19] as well as cytokine receptors such as the erythropoietin receptor (EpoR) and the interleukin-3 receptor. 14,20 We focused on the relationship between the ligand concentration (dose) and the area under the receptor activation curve (see Fig. 1B).…”

“…This time-integral represents the cumulative activation of the receptor over time and depends on the duration and peak of activation. The area under the activation curve is a good measure of a transient response and has proven useful to study signaling cascades, 21,22 reaction-diffusion systems, 23 cell surface receptors, 20 and was also found to link ERK activation with DNA synthesis. 24 To study the input-output relationship of cellular signaling models, simulations are typically used to obtain sensitivities of outputs on the different model components.…”

Modelling of multi-component immunoassay kinetics — A new node-based method for simulation of complex assays

“…These chemical reaction networks can be used to describe complex physicochemical systems by utilizing quality and quantitative information [18,19,20,21,22,23,24]. The network theories are widely used to describe metabolic pathways and signalling pathways [18,25,26,27,28]. Also, the modelling of enzymatic reaction cascades is of interest in drug development mainly to get a better overall view of the biological processes.…”

Computational approaches for modeling regulatory cellular networks

“…Because of the resources required for simulation of stochastic systems, spatially heterogeneous stochastic models are seldom used in modeling biological systems. Nevertheless, some stochastic models have been developed in specialized cases such as signaling in dendrites, immunological synapses, or endocytic vesicles [36][37][38].…”

“…Because of the resources required for simulation of stochastic systems, spatially heterogeneous stochastic models are seldom used in modeling biological systems. Nevertheless, some stochastic models have been developed in specialized cases such as signaling in dendrites, immunological synapses, or endocytic vesicles [36][37][38].The choice of modeling methodology will depend on the cellular process that is being investigated and the experimental tools that are available. In many cases, deterministic models that presume the existence of a well-stirred reactor are a good first approximation.…”