Trajectory reweighting for non-equilibrium steady states

Patrick B. Warren*, Rosalind J. Allen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Modern methods for sampling rugged landscapes in state space mainly rely on knowledge of the relative probabilities of microstates, which is given by the Boltzmann factor for equilibrium systems. In principle, trajectory reweighting provides an elegant way to extend these algorithms to non-equilibrium systems, by numerically calculating the relative weights that can be directly substituted for the Boltzmann factor. We show that trajectory reweighting has many commonalities with Rosenbluth sampling for chain macromolecules, including practical problems which stem from the fact that both are iterated importance sampling schemes: for long trajectories the distribution of trajectory weights becomes very broad and trajectories carrying high weights are infrequently sampled, yet long trajectories are unavoidable in rugged landscapes. For probing the probability landscapes of genetic switches and similar systems, these issues preclude the straightforward use of trajectory reweighting. The analogy to Rosenbluth sampling suggests though that path-ensemble methods such as PERM (pruned-enriched Rosenbluth method) could provide a way forward.

Original languageEnglish
Pages (from-to)3104-3113
Number of pages10
JournalMolecular Physics
Issue number21-22
Early online date9 May 2018
Publication statusPublished - 17 Nov 2018


  • 05.10.-a
  • 05.40.-a
  • 05.70.Ln
  • Monte-Carlo
  • non-equilibrium steady state
  • path sampling


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