Functional aspects of the EGF-induced MAP kinase cascade: a complex self-organizing system approach

Abstract

The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a "surface" and an "internalized" parallel route, we use systems biology techniques to characterize aspects of the network's functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system's output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

Figure 1. Visual comparison of protein groupings.

The groupings for different levels of EGF concentration were compared against each other by means of VI-metric and the obtained results are presented geometrically, by means of MDS, in a space of reduced dimensions. a) A 2D display reflecting all the pairwise distances between the 101 distinct groupings. Note that there is overlap between clusterings for consecutive values of EGF concentration. b) To resolve this overlap, the 1st dimension of the MDS map (r1), has been plotted as a function of [EGF].

Figure 2. Graphical correspondence between groupings.

A schematic representation of how functional groups change from a compositional perspective. The protein groupings for [EGF] = 5, 250, 5000 are compared in pairs. Groups have been ordered in terms of compactness. The color indicates the order of the groups with blue corresponding to the strongest functional cluster. Lines connect the proteins that change group, with the change of [EGF] level.

Figure 3. A semantic map of protein-groupings.

The functional clusters, derived for three different levels of EGF concentration (a:5, b:250, c:5000), are presented over a graphical outline of the protein network. Proteins in the same group are sharing the same color, while the color of each group indicates the order of the group regarding compactness.

Figure 4. Protein groups from Consensus clustering.

a) An “aggregated” clustering in which the protein groups have been ranked according to a score reflecting the mutual coincidence of their members across the 101 different clusterings. The 5th group was the least coherent group and the ‘x’ symbol indicates that its compactness was at the “chance” level. Sos protein appeared as isolated from the rest network. b) The profiles of 6 representative proteins for different levels of EGF concentration.

Figure 5. Network vulnerabilty map.

Based on simulations, and at the level of [EGF] = 5000, we studied the effect of “removing” each protein on the network functionality. Using the selected (MaxRateERKppOR ERKipp) index, we assigned a score to each protein that reflects the change in that index. These scores were used to rank the proteins and group them according to the type (activation/deactivation) and strength of influences”.