The stochastic modelling of biological systems is informative and often very adequate, but it may easily be more expensive than other modelling approaches, such as differential equations. We present StochKit-FF, a parallel version of StochKit, a reference toolkit for stochastic simulations. StochKit-FF is based on the FastFlow programming toolkit for multicores and on the novel concept of selective memory. We experiment StochKit-FF on a model of HIV infection dynamics, with the aim of extracting information from efficiently run experiments, here in terms of average and variance and, on a longer term, of more structured data. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Aldinucci, M., Bracciali, A., Liò, P., Sorathiya, A., & Torquati, M. (2011). StochKit-FF: Efficient systems biology on multicore architectures. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6586 LNCS, pp. 167–175). https://doi.org/10.1007/978-3-642-21878-1_21
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