Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories

The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation—by orders of magnitude for some observables

Efficient Estimation of Rare-Event Kinetics

The efficient calculation of rare-event kinetics in complex dynamical systems, such as the rate and pathways of ligand dissociation from a protein, is a generally unsolved problem. Markov state models can systematically integrate ensembles of short simulations and thus effectively parallelize the computational effort, but the rare events of interest still need to be spontaneously sampled in the data. Enhanced sampling approaches, such as parallel tempering or umbrella sampling, can accelerate the computation of equilibrium expectations massively, but sacrifice the ability to compute dynamical expectations. In this work we establish a principle to combine knowledge of the equilibrium distribution with kinetics from fast “downhill” relaxation trajectories using reversible Markov models. This approach is general, as it does not invoke any specific dynamical model and can provide accurate estimates of the rare-event kinetics. Large gains in sampling efficiency can be achieved whenever one direction of the process occurs more rapidly than its reverse, making the approach especially attractive for downhill processes such as folding and binding in biomolecules. Our method is implemented in the PyEMMA software

Weighted ensemble: Recent mathematical developments

The weighted ensemble (WE) method, an enhanced sampling approach based on periodically replicating and pruning trajectories in a set of parallel simulations, has grown increasingly popular for computational biochemistry problems, due in part to improved hardware and the availability of modern software. Algorithmic and analytical improvements have also played an important role, and progress has accelerated in recent years. Here, we discuss and elaborate on the WE method from a mathematical perspective, highlighting recent results which have begun to yield greater computational efficiency. Notable among these innovations are variance reduction approaches that optimize trajectory management for systems of arbitrary dimensionality.Comment: 12 pages, 10 figure

Efficient Sampling in Stochastic Biological Models

Even when the underlying dynamics are known, studying the emergent behavior of stochastic biological systems in silico can be computationally intractable, due to the difficulty of comprehensively sampling these models. This thesis presents the study of two techniques for efficiently sampling models of complex biological systems. First, the weighted ensemble enhanced sampling technique is adapted for use in sampling chemical kinetics simulations, as well as spatially resolved stochastic reaction-diffusion kinetics. The technique is shown to scale to large, cell-scale simulations, and to accelerate the sampling of observables by orders of magnitude in some cases. Second, I study the free energy estimates of peptides and proteins using Markov random fields. These graphical models are constructed from physics-based forcefields, uniformly sampled at different densities in dihedral angle space, and free energy estimates are computed using loopy belief propagation. The effect of sample density on the free energy estimates provided by loopy belief propagation is assessed, and it is found that in most cases a modest increase in sample density leads to significant improvement in convergence. Additionally, the approximate free energies from loopy belief propagation are compared to statistically exact computations and are confirmed to be both accurate and orders of magnitude faster than traditional methods in the models assessed

Simulating rare events using a Weighted Ensemble-based string method

We introduce an extension to the Weighted Ensemble (WE) path sampling method to restrict sampling to a one dimensional path through a high dimensional phase space. Our method, which is based on the finite-temperature string method, permits efficient sampling of both equilibrium and non-equilibrium systems. Sampling obtained from the WE method guides the adaptive refinement of a Voronoi tessellation of order parameter space, whose generating points, upon convergence, coincide with the principle reaction pathway. We demonstrate the application of this method to several simple, two-dimensional models of driven Brownian motion and to the conformational change of the nitrogen regulatory protein C receiver domain using an elastic network model. The simplicity of the two-dimensional models allows us to directly compare the efficiency of the WE method to conventional brute force simulations and other path sampling algorithms, while the example of protein conformational change demonstrates how the method can be used to efficiently study transitions in the space of many collective variables

Theory and Simulation of Rare Events in Stochastic Systems

Activated processes driven by rare fluctuations are discussed in this thesis. Understanding the dynamics of these activated processes is important for understanding chemical and biological reactions, drug design and many other important applications. First, theoretical tools including the Langevin equation, the Fokker-Planck equation and the path integral technique are reviewed. Based on these theories, simulation methods have been developed to sample the activated processes by a number of investigators. Several of the most important path sampling and path generating approaches are introduced. A combination of analytic and numerical techniques are applied to study the distribution of the durations of transition events over a barrier in a one-dimensional system undergoing over-damped Langevin dynamics. Then we employ the ``weighted ensemble' path sampling method to generate an unbiased ensemble of paths for a conformational transition in a 210-dimensional model of the protein calmodulin, and also find the reaction rate. The results show that the weighted ensemble approach is a remarkably straightforward and successful method. At last, systems with multiple channels are studied by the weighted ensemble approach and the more common transition path sampling approach. The weighted ensemble method is distinguished by its ability to perform complete path sampling for systems with multiplechannels at reasonable cost