%0 Journal Article %1 Donovan2016SystemsBioWE %A Donovan, Rory M. %A Tapia, Jose-Juan %A Sullivan, Devin P. %A Faeder, James R. %A Murphy, Robert F. %A Dittrich, Markus %A Zuckerman, Daniel M. %D 2016 %I Public Library of Science %J PLOS Computational Biology %K chemical-reaction-network systems-biology weighted-ensemble %N 2 %P 1-25 %R 10.1371/journal.pcbi.1004611 %T Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories %U http://dx.doi.org/10.1371%2Fjournal.pcbi.1004611 %V 12 %X Author Summary Stochastic simulations (simulations where randomness plays a role) of even simple biological systems are often so computationally intensive that it is impossible, in practice, to simulate them exhaustively and gather good statistics about the likelihood of different outcomes. The difficulty is compounded for the observation of rare events in these simulations; unfortunately, rare events, such as state transitions and barrier crossings, are often those of particular interest. Using the weighted ensemble (WE) method, we are able to enhance the characterization of rare events in cell biology simulations, but in such a way that the statistics for these events remain unbiased. The histogram of outcomes that WE produces has the same shape as a naive one, but the resolution of events in the tails of the histogram is greatly improved. This improved resolution in rare event statistics can be used to infer unbiased estimates of long timescale dynamics from short simulations, and we show that using a weighted ensemble can result in a reduction in total simulation time needed to sample certain events of interest in spatial, stochastic models of biological systems.

@article{Donovan2016SystemsBioWE, abstract = {Author Summary Stochastic simulations (simulations where randomness plays a role) of even simple biological systems are often so computationally intensive that it is impossible, in practice, to simulate them exhaustively and gather good statistics about the likelihood of different outcomes. The difficulty is compounded for the observation of rare events in these simulations; unfortunately, rare events, such as state transitions and barrier crossings, are often those of particular interest. Using the weighted ensemble (WE) method, we are able to enhance the characterization of rare events in cell biology simulations, but in such a way that the statistics for these events remain unbiased. The histogram of outcomes that WE produces has the same shape as a naive one, but the resolution of events in the tails of the histogram is greatly improved. This improved resolution in rare event statistics can be used to infer unbiased estimates of long timescale dynamics from short simulations, and we show that using a weighted ensemble can result in a reduction in total simulation time needed to sample certain events of interest in spatial, stochastic models of biological systems.}, added-at = {2017-03-01T22:13:32.000+0100}, author = {Donovan, Rory M. and Tapia, Jose-Juan and Sullivan, Devin P. and Faeder, James R. and Murphy, Robert F. and Dittrich, Markus and Zuckerman, Daniel M.}, biburl = {https://www.bibsonomy.org/bibtex/210464d4ff5f3c0aebf14d255031f7673/salotz}, description = {Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories}, doi = {10.1371/journal.pcbi.1004611}, interhash = {e0c77f147aa201eb80bf21bf2e967468}, intrahash = {10464d4ff5f3c0aebf14d255031f7673}, journal = {PLOS Computational Biology}, keywords = {chemical-reaction-network systems-biology weighted-ensemble}, month = {02}, number = 2, pages = {1-25}, publisher = {Public Library of Science}, timestamp = {2017-03-01T22:13:32.000+0100}, title = {Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories}, url = {http://dx.doi.org/10.1371%2Fjournal.pcbi.1004611}, volume = 12, year = 2016 }