Description: Complex biological networks, such as intracellular signaling networks, are modeled by the evolution to accomplish a variety of different regulatory functions. This is achieved by controlling the overall topology of the network which, then, affects its dynamic behavior. Biological networks are hierarchichal, scale-free, modular structure in which few nodes, the hubs, play a particularly relevant topological role and this may reflect a critical role at biological level. However, also nodes with no or lower hub role may have critical regulatory role in certain biological phenomena. This could reflect node-specific topological properties. Thus, it is of interest to categorize every nodes in a network by means of topological parameters allowing scoring of the nodes according to their individual topological relevance. Computation of centrality indexes may accomplish this goal. The centrality indexes are topological parameters allowing a node-by-node quantification of the reciprocal relationship between the nodes. This provides a classification of the nodes according to their capability to influence the function of other nodes in the network. Combination of this analysis with experimental data (node attributes) may help to identify critical nodes and regulatory circuits in a context-specific manner.

Supervisors: Dr. Giovanni Scardoni.
License: GNU General Public Licence

Description: MetaPlab is a virtual laboratory which aims at assisting modelers both to understand the internal mechanisms of biological systems and to forecast, in silico, their response to external stimuli, environmental condition alterations or structural changes. MetaPlab framework is based on a core module which enables to design and manage biological models, and an extensible set of plugins by which MP models can be generated, optimized, simulated and analyzed.

Supervisors: Prof. Vincenzo Manca, Dr. Alberto Castellini, Dr. Luca Marchetti.
License: GNU General Public Licence