title: Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories.
abstract: 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.
authors: Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Rory M
authors: RM
authors: Donovan
authors: Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America.|Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Jose-Juan
authors: JJ
authors: Tapia
authors: Joint CMU-Pitt Ph.D. Program in Computational Biology, Pittsburgh, Pennsylvania, United States of America.|Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors: Devin P
authors: DP
authors: Sullivan
authors: Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: James R
authors: JR
authors: Faeder
authors: Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America.
authors: Robert F
authors: RF
authors: Murphy
authors: Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.|Pittsburgh Supercomputing Center, Pittsburgh, Pennsylvania, United States of America.
authors: Markus
authors: M
authors: Dittrich
authors: Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
authors: Daniel M
authors: DM
authors: Zuckerman
mesh_terms: D000465:Algorithms; D000818:Animals; D001001:Anura; D008954:Models, Biological; D009469:Neuromuscular Junction / Q000502:physiology; D013269:Stochastic Processes; D049490:Systems Biology / Q000379:methods*