Divide and conquer, faster: speeding up molecular simulations.

In this issue of the Biophysical Journal, Biedermann et al. (1) describe a new computational package that implements a split GPU-CPU algorithm for speeding up the calculations of particle dynamics and reaction kinetics in heterogeneous biomolecular mixtures. The movement and interactions of proteins and other biomolecules are central to understanding drug action and improving drug design. Computational models of these molecular movements and reactions are indispensable, and many different simulation scales are available (Fig. 1 A), from atom-level resolution using molecular-dynamics packages such as CHARMM (www.charmm.org) (2) to continuum reaction-diffusion models at the tissue level (3) and compartmental models at the whole-body level (4). Figure 1 Simulation of heterogeneous molecular mixtures. (A) As the level of particle detail and particle discrimination increases (to the right), the computational time increases, while the ability to simulate large volumes and long times decreases. (B) Simplified ... If we focus on cell-level and subcellular-level simulations, then low-protein-copy number, spatial crowding, compartmentalization, and heterogeneity can make stochasticity a key consideration. At this scale, discriminating between the individual molecules becomes more important, and continuum approaches cannot do this. Methods that can explicitly simulate individual molecules are computationally costly; one approach to reduce the cost is to couple different model types. For example, molecular-dynamics simulations over short times can be used to estimate binding and unbinding energies; these can then be used in continuum models to estimate the longer-term behavior of a population of molecules in response to a perturbation. However, in many cases, to understand the system we will want to track each of the molecules individually; for these purposes the appropriate simulation scale is not atomistic, but instead that of individual molecular particles, each trackable over time as it moves and becomes involved in intermolecular reactions. Several packages have been developed to facilitate this level of simulation of individual particle movement and reaction, including MCell by groups at Salk Institute for Biological Studies (La Jolla, CA) and the University of Pittsburgh (Pittsburgh, PA; mcell.org) (5); Smoldyn by the Andrews group at the Fred Hutchinson Cancer Research Center (Seattle, WA; www.smoldyn.org) (6); and ReaDDy by the Noe group at the Free University of Berlin (Berlin, Germany; www.readdy-project.org) (7). Several of these modules have been or are being integrated into the larger simulation environments that model cell signaling, e.g., Spatiocyte in E-Cell (www.e-cell.org) and Smoldyn in VCell (University of Connecticut Health Center, Farmington, CT; www.nrcam.uchc.edu). Packages that use a lattice-based movement system and do not discriminate between all particles are also available, such as the following: MesoRD by the Elf lab at Uppsala University (Uppsala, Sweden; http://mesord.sourceforge.net/); StochSim by the Bray group at The University of Cambridge (Cambridge, United Kingdom; http://sourceforge.net/projects/stochsim/). Spatiocyte by the Tomita group at the Institute for Advanced Biosciences, Keio University (Fujisawa, Japan; spatiocyte.org) (8) uses a lattice, but individual molecules are still represented. The key problem with this level of simulation—tracking individual particles—is the computational cost; the time required to simulate all proteins within a single cell can be prohibitive. This cost increases with particle density, with simulation volume, and with the complexity and heterogeneity of the spatial domain. For the most part, this computational cost is not due to the calculation of the reactions that the proteins undergo with each other, but instead to the particle dynamics. Methods to speed up the calculation of particle dynamics could greatly enhance the ability to simulate subcellular processes such as signaling. Biedermann et al. (1), in this issue, report a new implementation of the ReaDDy package, ReaDDyMM, that leverages OpenMM and splits the calculations—the particle dynamics (movement, e.g., Brownian dynamics) to be calculated by the GPU and the reactions to be calculated by the CPU (Fig. 1 B). Three conditions must be met for this to obtain the reported two-orders of magnitude speedup, as follows. 1. There must be considerable separation in timescales between the particle dynamics and the reaction kinetics. ReaDDyMM takes advantage of the separation in timescales by running many smaller timesteps of particle dynamics for each larger timestep of reaction kinetics (Fig. 1 C). If this were not the case, then communication between the processors would make the algorithm inefficient. Greater timescale separation—faster diffusion, slower reactions—allows increased n (the number of GPU dynamics steps per CPU kinetics step; Fig. 1 B). 2. The level of speedup increases with particle density. At lower densities, both the overall computational time and the speedup are lower. This methodology thus appears particularly suited to understanding highly crowded biological environments. 3. Both the diffusion timestep (τ) and the reaction timestep (nτ) must be less than their corresponding characteristic timescales; otherwise, accuracy can be severely affected. ReaDDyMM split CPU-GPU calculation is distinct from Smoldyn’s GPU implementation, in which both diffusion and reactions are calculated on the GPU (9), and thus may provide an improvement for complex reaction networks. Benchmarking for both accuracy and computation time in various scenarios may indicate application-specific benefits to one approach over the other. Are two orders of magnitude of speedup sufficient to make this form of molecular simulation feasible? The answer depends on the application. The volumes simulated in the presented examples (1) are small—an attoLiter, or one-thousandth of an Escherichia coli cell. Mammalian cells are larger still. The protein densities used are similar to the total protein densities in E. coli. Thus, simulating a whole cell using this method would take one or more weeks, but this may have the potential to be further improved through parallelization. The benefit is the level of detail and accuracy possible, and for subcellular components this method will be both quick and highly detailed. ReaDDyMM is a useful advance and increases the tools and feature sets available for simulation of heterogeneous multimolecular simulations. This is an opportune time for such simulation packages, as superresolution microscopy and other advances make possible the experimental tracking of individual molecules within a complex cellular context.