Identifying sets of key players in a social network
Computational & Mathematical Organization Theory
Bioinformatics
On Parallel Stochastic Simulation of Diffusive Systems
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
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Recent advances in the stochastic simulation of biological systems have exploited the weighted dependency di-graph as a compact representation of the computational workload. It was largely used to represent the causal relationships among reactions and then to determine their cause-effect implications. Although critical for several applications, the topology of the dependency graph has been little studied so far. Here, we make use of some network topology indices to detect and characterize the important reactions of two real case studies. We measure the stability of such indices over time and make a case for considering them in parallel stochastic simulation.