English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Selected-node stochastic simulation algorithm.

MPS-Authors
/cone/persons/resource/persons219123

Duso,  Lorenzo
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

/cone/persons/resource/persons219805

Zechner,  Christoph
Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Duso, L., & Zechner, C. (2018). Selected-node stochastic simulation algorithm. The Journal of chemical physics, 148(16): 164108. doi:10.1063/1.5021242.


Cite as: https://hdl.handle.net/21.11116/0000-0003-F5DD-0
Abstract
Stochastic simulations of biochemical networks are of vital importance for understanding complex dynamics in cells and tissues. However, existing methods to perform such simulations are associated with computational difficulties and addressing those remains a daunting challenge to the present. Here we introduce the selected-node stochastic simulation algorithm (snSSA), which allows us to exclusively simulate an arbitrary, selected subset of molecular species of a possibly large and complex reaction network. The algorithm is based on an analytical elimination of chemical species, thereby avoiding explicit simulation of the associated chemical events. These species are instead described continuously in terms of statistical moments derived from a stochastic filtering equation, resulting in a substantial speedup when compared to Gillespie's stochastic simulation algorithm (SSA). Moreover, we show that statistics obtained via snSSA profit from a variance reduction, which can significantly lower the number of Monte Carlo samples needed to achieve a certain performance. We demonstrate the algorithm using several biological case studies for which the simulation time could be reduced by orders of magnitude.