Showing papers by "Steven J. Plimpton published in 2008"

A constant-time kinetic Monte Carlo algorithm for simulation of large biochemical reaction networks

Abstract: The time evolution of species concentrations in biochemical reaction networks is often modeled using the stochastic simulation algorithm SSAGillespie, J. Phys. Chem. 81, 2340 1977. The computational cost of the original SSA scaled linearly with the number of reactions in the network. Gibson and Bruck developed a logarithmic scaling version of the SSA which uses a priority queue or binary tree for more efficient reaction selection Gibson and Bruck, J. Phys. Chem. A 104, 1876 2000. More generally, this problem is one of dynamic discrete random variate generation which finds many uses in kinetic Monte Carlo and discrete event simulation. We present here a constant-time algorithm, whose cost is independent of the number of reactions, enabled by a slightly more complex underlying data structure. While applicable to kinetic Monte Carlo simulations in general, we describe the algorithm in the context of biochemical simulations and demonstrate its competitive performance on small- and medium-size networks, as well as its superior constant-time performance on very large networks, which are becoming necessary to represent the increasing complexity of biochemical data for pathways that mediate cell function. © 2008 American Institute of Physics. DOI: 10.1063/1.2919546

Substructured molecular dynamics using multibody dynamics algorithms

Abstract: This paper reports a new research effort aimed at using efficient multibody dynamics methods to simulate coarse-grained molecular systems. Various molecular systems are studied and the results of nanosecond-long simulations are analyzed to validate the method. The systems studied include bulk water, alkane chains, alanine dipeptide and carboxyl terminal fragments of calmodulin, ribosomal L7/L12 and rhodopsin proteins. The stability and validity of the simulations are studied through conservation of energy, thermodynamics properties and conformational analysis. In these simulations, a speed up of an order of magnitude is realized for conservative error bounds with a fixed timestep integration scheme. A discussion is presented on the open-source software developed to facilitate future research using multibody dynamics with molecular dynamics.

Nanoparticle flow, ordering and self-assembly.

Abstract: Nanoparticles are now more than ever being used to tailor materials function and performance in differentiating technologies because of their profound effect on thermo-physical, mechanical and optical properties. The most feasible way to disperse particles in a bulk material or control their packing at a substrate is through fluidization in a carrier, followed by solidification through solvent evaporation/drying/curing/sintering. Unfortunately processing particles as concentrated, fluidized suspensions into useful products remains an art largely because the effect of particle shape and volume fraction on fluidic properties and suspension stability remains unexplored in a regime where particle-particle interaction mechanics is prevalent. To achieve a stronger scientific understanding of the factors that control nanoparticle dispersion and rheology we have developed a multiscale modeling approach to bridge scales between atomistic and molecular-level forces active in dense nanoparticle suspensions. At the largest length scale, two 'coarse-grained' numerical techniques have been developed and implemented to provide for high-fidelity numerical simulations of the rheological response and dispersion characteristics typical in a processing flow. The first is a coupled Navier-Stokes/discrete element method in which the background solvent is treated by finite element methods. The second is a particle based method known as stochastic rotational dynamics. These two methods providemore » a new capability representing a 'bridge' between the molecular scale and the engineering scale, allowing the study of fluid-nanoparticle systems over a wide range of length and timescales as well as particle concentrations. To validate these new methodologies, multi-million atoms simulations explicitly including the solvent have been carried out. These simulations have been vital in establishing the necessary 'subgrid' models for accurate prediction at a larger scale and refining the two coarse-grained methodologies.« less