relation: http://d-scholarship-dev.library.pitt.edu/28507/
title: Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories
creator: Donovan, RM
creator: Tapia, JJ
creator: Sullivan, DP
creator: Faeder, JR
creator: Murphy, RF
creator: Dittrich, M
creator: Zuckerman, DM
description: The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables.
date: 2016-02-01
type: Article
type: PeerReviewed
format: application/pdf
language: en
rights: attached
identifier: http://d-scholarship-dev.library.pitt.edu/28507/1/journal.pcbi.1004611.PDF
format: text/plain
language: en
rights: attached
identifier: http://d-scholarship-dev.library.pitt.edu/28507/3/licence.txt
identifier:   Donovan, RM and Tapia, JJ and Sullivan, DP and Faeder, JR and Murphy, RF and Dittrich, M and Zuckerman, DM  (2016) Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories.  PLoS Computational Biology, 12 (2).   ISSN 1553-734X     
relation: 10.1371/journal.pcbi.1004611