CSDL Home IEEE/ACM Transactions on Computational Biology and Bioinformatics 2012 vol.9 Issue No.03 - May-June
Issue No.03 - May-June (2012 vol.9)
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
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.
Stochastic simulation, parallel computing, graphs and network, systems biology.
Tommaso Mazza, Paolo Ballarini, Rosita Guido, Davide Prandi, "The Relevance of Topology in Parallel Simulation of Biological Networks", IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.9, no. 3, pp. 911-923, May-June 2012, doi:10.1109/TCBB.2012.27