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-eect implica- tions. Although critical for several applications, the topol- ogy of the dependency graph has been little studied so far. Here, we make use of some network topology indices to de- tect 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.
Stability analysis of biological network topologies during stochastic simulation
Prandi, D.
2011-01-01
Abstract
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-e ect implica- tions. Although critical for several applications, the topol- ogy of the dependency graph has been little studied so far. Here, we make use of some network topology indices to de- tect 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
