GRODYNET

Group-based Dynamics on Complex Networks

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 Obiettivo del progetto (Objective)

'Cells, ecosystems and economies are examples of complex systems. In complex systems, individual components interact with each other, usually in nonlinear ways, giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction topology, complex systems cannot be properly understood by just analyzing their constituent parts, which poses important challenges from both a fundamental perspective and an 'application' perspective.

The structure of the network of interactions was traditionally ignored and approximated by one of two limiting cases: a regular low-dimensional lattice or a completely random uncorrelated graph. It wasn't until recently that the scientific community, spearheaded by physicists, started to look for universality on the statistical description and classification of networks. Although significant progress has been made since then, we are still far from the ultimate goal of developing a general theory decribing the impact of network structure on the dynamics of complex systems.

The general aims of the project are: (i) to develop a general theoretical framework, based on the mesoscopic structure of networks, to understand the interplay between network structure and system dynamics; and (ii) to apply this framework to biological and socio-economic systems of interest.'

Introduzione (Teaser)

European scientists are building a model that would aid in the analysis of complex systems by dissecting the structure and interaction of individual components. This tool could find application in a wide range of complex systems such as ecosystems, cells or even economies.

Descrizione progetto (Article)

Often, in order to understand complex systems, it is not sufficient to just analyse their constituent parts. It is now well accepted that an in-depth statistical description and classification is required for the network of interactions. However, a general theory describing the impact of network structure on the dynamics of complex systems is yet to be developed.

The key objective of the EU-funded 'Group-based dynamics on complex networks' (GRODYNET) project is to develop a general theoretical framework that could be utilised to understand the interplay between network structure and system dynamics. The long-term goal is to apply this framework to biological and socioeconomic systems of interest.

Using group-based models to predict the evolution of networks, scientists have succeeded in extending these tools for systems that are not network systems per se, such as for predicting human decisions. They have also confirmed the importance of group rather than individual nodes on network dynamics.

This same model has been applied to understand cell polarity in yeast and delineate the meso-scale interactions of proteins implicated in the network. Researchers have demonstrated that a model with a single kinetic constant for each group of proteins is capable of predicting the dynamics of individual proteins.

The divergent applicability of the GROYDYNET model is expected to aid the analysis of complex systems from a fundamental and application-related perspective.