Free Energy Monte Carlo Simulations on a Distributed Network

While the use of enhanced sampling techniques and parallel computing to determine potentials of mean force is in widespread use in modern Molecular Dynamics and Monte Carlo simulation studies, there have been few methods that efficiently combine heterogeneous computer resources of varying quality and speeds in realizing a single simulation result on a distributed network. Here, we apply an algorithm based on the Monte Carlo method of Wang and Landau within a client-server framework, in which individual computing nodes report a histogram of regions of phase space visited and corresponding updates to a centralized server at regular intervals entirely asynchronously. The server combines the data and reports the sum to all nodes so that the overall free energy determination scales linearly with the total amount of resources allocated. We discuss our development of this technique and present results for molecular simulations of DNA.

[1]  F. Calvo,et al.  Sampling along reaction coordinates with the Wang-Landau method , 2002, cond-mat/0205428.

[2]  Alexander N Morozov,et al.  Accuracy and convergence of the Wang-Landau sampling algorithm. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Alan M. Ferrenberg,et al.  Optimized Monte Carlo data analysis. , 1989, Physical Review Letters.

[4]  David Swigon,et al.  Theory of sequence-dependent DNA elasticity , 2003 .

[5]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[6]  Cees Dekker,et al.  Dual architectural roles of HU: formation of flexible hinges and rigid filaments. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Derek Y. C. Chan,et al.  A scalable parallel Monte Carlo method for free energy simulations of molecular systems , 2005, J. Comput. Chem..

[8]  A. Laio,et al.  Metadynamics: a method to simulate rare events and reconstruct the free energy in biophysics, chemistry and material science , 2008 .

[9]  V. Bloomfield DNA condensation by multivalent cations. , 1997, Biopolymers.

[10]  Derek Y. C. Chan,et al.  Effect of Chain Stiffness on Polyelectrolyte Condensation , 2005 .

[11]  M Müller,et al.  Avoiding boundary effects in Wang-Landau sampling. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Hwee Kuan Lee,et al.  Convergence and refinement of the Wang-Landau algorithm , 2006, Comput. Phys. Commun..

[13]  Gerald S. Manning,et al.  Counterion binding in polyelectrolyte theory , 1979 .

[14]  Christophe Chipot,et al.  Exploring Multidimensional Free Energy Landscapes Using Time-Dependent Biases on Collective Variables. , 2010, Journal of chemical theory and computation.

[15]  D. Landau,et al.  Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.

[16]  David Swigon,et al.  Effects of the nucleoid protein HU on the structure, flexibility, and ring-closure properties of DNA deduced from Monte Carlo simulations. , 2008, Journal of molecular biology.

[17]  David Swigon,et al.  Sequence-Dependent Effects in the Cyclization of Short DNA. , 2006, Journal of chemical theory and computation.

[18]  A. Voter Parallel replica method for dynamics of infrequent events , 1998 .

[19]  J. Bond,et al.  Grand canonical Monte Carlo molecular and thermodynamic predictions of ion effects on binding of an oligocation (L8+) to the center of DNA oligomers. , 1995, Biophysical journal.