Transition matrix Monte Carlo method

Wang, Jian‐Sheng

doi:10.1016/s0010-4655(99)00270-2

Cited by 43 publications

(38 citation statements)

References 42 publications

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“…During the same period of time this way of sampling has also been approached in a similar, but not identical, way by others, see e.g. [dO98], [BH98] or [WS02]. Our approach is in some sense more basic since we avoid involving any specific method of state generation in our scheme, but the methods make use of the same underlying structures.…”

Section: Introductionmentioning

confidence: 94%

A Monte Carlo Sampling Scheme for the Ising Model

Häggkvist

Rosengren

Andrén

et al. 2004

Journal of Statistical Physics

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In this paper we describe a Monte Carlo sampling scheme for the Ising model and similar discrete state models. The scheme does not involve any particular method of state generation but rather focuses on a new way of measuring and using the Monte Carlo data.We show how to reconstruct the entropy S of the model, from which e.g the free energy can be obtained. Furthermore we discuss how this scheme allows us to more or less completely remove the effects of critical fluctuations near the critical temperature and likewise how it reduces critical slowing down. This makes it possible to use simple state generation methods like the Metropolis algorithm also for large lattices.

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Section: Introductionmentioning

confidence: 94%

A Monte Carlo Sampling Scheme for the Ising Model

Häggkvist

Rosengren

Andrén

et al. 2004

Journal of Statistical Physics

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“…The simulation of an optimized ensemble results in equilibration times that can be substantially lower compared to other extended ensemble simulations that aim at sampling a flat histogram in the respective reaction coordinate [1,2]. Flat-histogram algorithms include the multicanonical method [3,4], broad histograms [5] and transition matrix Monte Carlo [6] when combined with entropic sampling, as well as the adaptive algorithm of Wang and Landau [7,8].…”

Section: Introductionmentioning

confidence: 99%

Feedback-optimized parallel tempering Monte Carlo

et al. 2006

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We introduce an algorithm to systematically improve the efficiency of parallel tempering Monte Carlo simulations by optimizing the simulated temperature set. Our approach is closely related to a recently introduced adaptive algorithm that optimizes the simulated statistical ensemble in generalized broad-histogram Monte Carlo simulations. Conventionally, a temperature set is chosen in such a way that the acceptance rates for replica swaps between adjacent temperatures are independent of the temperature and large enough to ensure frequent swaps. In this paper, we show that by choosing the temperatures with a modified version of the optimized ensemble feedback method we can minimize the round-trip times between the lowest and highest temperatures which effectively increases the efficiency of the parallel tempering algorithm. In particular, the density of temperatures in the optimized temperature set increases at the "bottlenecks" of the simulation, such as phase transitions. In turn, the acceptance rates are now temperature dependent in the optimized temperature ensemble. We illustrate the feedback-optimized parallel tempering algorithm by studying the two-dimensional Ising ferromagnet and the two-dimensional fully-frustrated Ising model, and briefly discuss possible feedback schemes for systems that require configurational averages, such as spin glasses.

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“…Examples include the multicanonical sampling, the 1/k-ensemble method [24], Wang-Landau sampling, and transition matrix Monte Carlo [25]. The initial weights are typically set to be equal to one, indicating an infinite-temperature setup of the system, and proposal moves leading to unvisited states are definitely accepted to enable exploration of the 220 whole energy spectrum.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

“…For instance, our work in this paper is motivated by certain membrane protein, TatA, as a key component in a protein translocation mechanism-the twin-arginine translocation (Tat) 25 pathway [5]. The aggregation of TatA is essential to the process in that it is believed to form the translocation channel [6].…”

Section: Background and Introductionmentioning

confidence: 99%

MBAR-enhanced lattice Monte Carlo simulation of the effect of helices on membrane protein aggregation

Rodger

2017

Journal of Computational Physics

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We study the effect of helical structure on the aggregation of proteins using a simplified lattice protein model with an implicit membrane environment. A recently proposed Monte Carlo approach, which exploits the proven statistical optimality of the MBAR estimator in order to improve simulation efficiency, was used. The results show that with both two and four proteins present, the tendency to aggregate is strongly expedited by the presence of amphipathic helix (APH), whereas a transmembrane helix (TMH) slightly disfavours aggregation.When four protein molecules are present, partially aggregated states (dimers and trimers) were more common when the APH was present, compared with the cases where no helices or only the TMH is present.Keywords: lattice Monte Carlo, parallel tempering, multicanonical sampling, protein aggregation, twin-arginine translocation Background and IntroductionProteins are macromolecules that are essential to the functioning of living organisms. The primary structure of a protein consists of a chain of amino acids that is coded by genes. Typically, upon synthesis on ribosomes, the extended chain must fold into a specific three-dimensional structure called the by one or more beads, coarse-grained models that balance accuracy and compu-2 tational efficiency, to atomistic models that are more accurate and detailed but too computationally prohibitive to study the full assembly process of interest.On the other hand, computational approaches can be applied to models of various resolutions, although certain methods may be more suitable to a particular of states from multiple equilibrium simulations using the MBAR estimator, and so there is a wide choice of methods that can be used in place of PT and MUCA as are used here.We emphasize that the TatA aggregation in the Tat mechanism serves as a motivation to the current lattice model, and that our simulation results are not

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Transition matrix Monte Carlo method

Cited by 43 publications

References 42 publications

A Monte Carlo Sampling Scheme for the Ising Model

A Monte Carlo Sampling Scheme for the Ising Model

Feedback-optimized parallel tempering Monte Carlo

MBAR-enhanced lattice Monte Carlo simulation of the effect of helices on membrane protein aggregation

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