Weighted Ensemble Simulation

Review of Methodology, Applications, and Software

Daniel Zuckerman, Lillian T. Chong

Research output: Contribution to journalReview article

24 Citations (Scopus)

Abstract

The weighted ensemble (WE) methodology orchestrates quasi-independent parallel simulations run with intermittent communication that can enhance sampling of rare events such as protein conformational changes, folding, and binding. The WE strategy can achieve superlinear scaling-the unbiased estimation of key observables such as rate constants and equilibrium state populations to greater precision than would be possible with ordinary parallel simulation. WE software can be used to control any dynamics engine, such as standard molecular dynamics and cell-modeling packages. This article reviews the theoretical basis of WE and goes on to describe successful applications to a number of complex biological processes-protein conformational transitions, (un)binding, and assembly processes, as well as cell-scale processes in systems biology. We furthermore discuss the challenges that need to be overcome in the next phase of WE methodological development. Overall, the combined advances in WE methodology and software have enabled the simulation of long-timescale processes that would otherwise not be practical on typical computing resources using standard simulation.

Original languageEnglish (US)
Pages (from-to)43-57
Number of pages15
JournalAnnual Review of Biophysics
Volume46
DOIs
StatePublished - May 22 2017

Fingerprint

Software
Proteins
Biological Phenomena
Systems Biology
Molecular Dynamics Simulation
Molecular dynamics
Rate constants
Sampling
Engines
Communication
Population

Keywords

  • Cell modeling
  • Kinetics
  • Molecular dynamics
  • Path sampling
  • Rare events
  • Weighted ensemble

ASJC Scopus subject areas

  • Biophysics
  • Structural Biology
  • Bioengineering
  • Biochemistry
  • Cell Biology

Cite this

Weighted Ensemble Simulation : Review of Methodology, Applications, and Software. / Zuckerman, Daniel; Chong, Lillian T.

In: Annual Review of Biophysics, Vol. 46, 22.05.2017, p. 43-57.

Research output: Contribution to journalReview article

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