Efficient exploration of discrete energy landscapes

Martin Mann, Konstantin Klemm

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

Many physical and chemical processes, such as folding of biopolymers, are best described as dynamics on large combinatorial energy landscapes. A concise approximate description of the dynamics is obtained by partitioning the microstates of the landscape into macrostates. Since most landscapes of interest are not tractable analytically, the probabilities of transitions between macrostates need to be extracted numerically from the microscopic ones, typically by full enumeration of the state space or approximations using the Arrhenius law. Here, we propose to approximate transition probabilities by a Markov chain Monte Carlo method. For landscapes of the number partitioning problem and an RNA switch molecule, we show that the method allows for accurate probability estimates with significantly reduced computational cost.

Original languageEnglish
Article number011113
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume83
Issue number1
DOIs
Publication statusPublished - Jan 18 2011
Externally publishedYes

Fingerprint

Energy Landscape
Partitioning
Biopolymers
Chemical Processes
Markov Chain Monte Carlo Methods
Physical process
Folding
Transition Probability
Enumeration
Computational Cost
Switch
State Space
Molecules
enumeration
energy
Markov chains
biopolymers
Approximation
Estimate
transition probabilities

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Statistical and Nonlinear Physics
  • Statistics and Probability

Cite this

Efficient exploration of discrete energy landscapes. / Mann, Martin; Klemm, Konstantin.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 83, No. 1, 011113, 18.01.2011.

Research output: Contribution to journalArticle

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