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Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

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

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Abstract

© 2016 Donovan et al. 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.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Donovan, RMdonovanr@pitt.eduDONOVANR
Tapia, JJ
Sullivan, DP
Faeder, JRfaeder@pitt.eduFAEDER
Murphy, RF
Dittrich, M
Zuckerman, DMddmmzz@pitt.eduDDMMZZ
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
EditorMeier-Schellersheim, MartinUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date: 1 February 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: PLoS Computational Biology
Volume: 12
Number: 2
DOI or Unique Handle: 10.1371/journal.pcbi.1004611
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computational Biology
School of Medicine > Computational Biology
School of Medicine > Computational and Systems Biology
Refereed: Yes
ISSN: 1553-734X
Date Deposited: 23 Aug 2016 13:44
Last Modified: 02 Feb 2019 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/28507

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