Computational modeling of the EGFR network elucidates control mechanisms regulating signal dynamics

Abstract

BackgroundThe epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language.ResultsOur analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization.ConclusionsThe insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.

The role of the microenvironment in tumor growth and invasion

“…Activated ERK also feeds back to the pathway activation at several levels (Shin et al , 2009). For example, it exerts a negative feedback effect by interfering with Ras activation through SOS phosphorylation (Bourhis et al , 1997; Wang et al , 2009a). …”

Static network structure can be used to model the phenotypic effects of perturbations in regulatory networks

“…They have also been used to predict the effect of perturbations, e.g. inhibiting one protein/gene or more, on other proteins/genes in the network (Li et al, 2010;Maslov and Ispolatov, 2007;Mitsos et al, 2009;Prill et al, 2010;Wang et al, 2009). For example, Ruths et al successfully predicted changes in protein levels in the MAPK/ AKT signaling network in breast tumor cells in response to perturbation of two genes (Ruths et al, 2008).…”

Review of the computational models for the signaling pathway

“…By the Law of Mass Action, a group of nonlinear ordinary differential equations are used to describe each reactant. Recently, significant progress has been made in the area of modeling for better understanding of the biological behavior of the cell signaling pathways [13][14][15][16]. Several research groups have used ODE to analyze the dynamics of signaling networks and generate experimentally testable predictions [17][18][19][20][21][22][23][24][25].…”