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

Kosmidis EK, Moschou V, Ziogas G, Boukovinas I, Albani M, Laskaris NA - PLoS ONE (2014)

Bottom Line: Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems.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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Physiology, Department of Medicine, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Greece.

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.

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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.
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pone-0111612-g004: 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.

Mentions: The results from consensus clustering are shown in Fig. 4 where protein groups have been ranked according to a score reflecting the mutual coincidence of their members across the 101 different clusterings. Five functional groups have been identified with the 5th being the least coherent, below the “chance” level (see Fig. S3). Sos protein appeared as a 6th single member group, isolated from the rest of the network.


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

Kosmidis EK, Moschou V, Ziogas G, Boukovinas I, Albani M, Laskaris NA - PLoS ONE (2014)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4221048&req=5

pone-0111612-g004: 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.
Mentions: The results from consensus clustering are shown in Fig. 4 where protein groups have been ranked according to a score reflecting the mutual coincidence of their members across the 101 different clusterings. Five functional groups have been identified with the 5th being the least coherent, below the “chance” level (see Fig. S3). Sos protein appeared as a 6th single member group, isolated from the rest of the network.

Bottom Line: Our results provide a new "vista" of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems.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.

View Article: PubMed Central - PubMed

Affiliation: Laboratory of Physiology, Department of Medicine, Aristotle University of Thessaloniki, University Campus, Thessaloniki, Greece.

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.

Show MeSH