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CNN special guest - Prof Peter Csermely

When Feb 24, 2012
from 12:30 PM to 01:30 PM
Where Trinity Hall - Lecture Theatre
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NB: Professor Csermely's slides are available here

In February, the regular CNN seminar will be replaced by a special event: a lecture by Professor Péter Csermely, visiting from the Department of Medical Chemistry, Semmelweis University, Budapest, Hungary. 

Professor Csermely will be speaking to CNN about "Novel network centrality and community measures – and their changes in crisis and adaptation".

Abstract: Our multidisciplinary group uses the general properties of networks as ‘highways’ making the transfer of concepts between various disciplines rather easy. This concept-transfer embeds the original idea to the entirely different context of another scientific field, and helps to solve creativity deadlocks. These generalizations allow the utilization of the ‘wisdom’ of biological systems surviving crisis events for many billions of years. As an example of network dynamics in biological systems the community structure of the protein-protein interaction network of yeast cells was studied using our Moduland program, which is a novel method family to detect pervasively overlapping communities (PLoS ONE 7, e12528). Upon heat shock the compactness of yeast protein communities increased and the number of inter-community bridges decreased (PLoS Comput. Biol. 7, e1002187). The stress-induced decrease of inter-modular connections was beneficial, since it A.) allowed a better focusing on vital functions, and thus spared resources; B.) localized damage to the affected communities; C.) reduced the propagation of noise; D.) allowed a larger ‘degree of freedom’ of the individual communities to explore different adaptation strategies; and E.) helped the ‘mediation of inter-community conflicts’ during a period of violent intra-community changes. Community reorganization emerged as general and novel systems level way of cost-efficient adaptation.

Inter-community nodes, such as the highly dynamic creative nodes play a particularly important role in adaptive processes. Besides the modular analysis above, highly influential nodes can be identified by their efficiency in perturbation-propagation using our recently developed Turbine program ( Game centrality, i.e. the ability of a node or edge to establish or break cooperation in a repeated social dilemma game was also shown to highlight influential nodes using our program NetworGame (, which is able to accommodate any real world networks using with any initial strategy distributions. Spatial games can also be rationalized in networks of non-conscious agents, such as amino acids, or proteins. Novel network centrality and community measures help the identification of key nodes determining the systems potential for fast adaptation.