The Relevance of Topology in Parallel Simulation of Biological Networks

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.