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.

Harnessing functional segregation across brain rhythms as a means to detect EEG oscillatory multiplexing during music listening

“…To facilitate the comparison among recording conditions, we derived prototypical segregation profiles as representatives for each ensemble of functional connectivity patterns (24 FCGs per condition). To this end, the well-known technique of Consensus-Clustering [49,50] was applied to each ensemble using the code freely available from a recent study [51]. In short, based on an aggregation scheme that worked at the level of edges (and used all the 24 partitioned FCGs), we estimated the probability of any pair of nodes to occur in the same module.…”