期刊
PLOS COMPUTATIONAL BIOLOGY
卷 12, 期 2, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pcbi.1004611
关键词
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资金
- National Science Foundation
- National institute of Health of NSF [MCB-1119091]
- NIH [P41 GM103712, T32 EB009403, R01 GM090033, R01 NS090644, R01 GM115805]
- NSF [CNS-0926181, 1249546]
- Div Of Molecular and Cellular Bioscience
- Direct For Biological Sciences [1701846] Funding Source: National Science Foundation
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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