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Issue No.03 - May-June (2012 vol.9)

pp: 911-923

Tommaso Mazza , IRCCS Casa Sollievo della Sofferenza, Rome

Paolo Ballarini , Ecole Centrale de Paris, Chatenay-Malabry, France

Rosita Guido , University of Calabria, Arcavacata de Rende

Davide Prandi , Laboratory of Computational Oncology, Mattarello

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2012.27

ABSTRACT

Important achievements in traditional biology have deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example, the increasing comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every "piece” of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.

INDEX TERMS

Stochastic simulation, parallel computing, graphs and network, systems biology.

CITATION

Tommaso Mazza, Paolo Ballarini, Rosita Guido, Davide Prandi, "The Relevance of Topology in Parallel Simulation of Biological Networks",

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