Journal
MOLECULAR PHYSICS
Volume 116, Issue 21-22, Pages 3104-3113Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00268976.2018.1471226
Keywords
Monte-Carlo; path sampling; non-equilibrium steady state
Funding
- Royal Society University Research Fellowship
- Engineering and Physical Sciences Research Council (EPSRC) [EP/I030298/1, EP/J007404/1]
- Higgs Centre for Theoretical Physics
- EPSRC [EP/I030298/1, EP/J007404/1] Funding Source: UKRI
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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. [GRAPHICS] .
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