We introduce the software package ReaDDy for simulation of detailed spatiotemporal mechanisms of dynamical processes in the cell, based on reaction-diffusion dynamics with particle resolution. In contrast to other particle-based reaction kinetics programs, ReaDDy supports particle interaction potentials. This permits effects such as space exclusion, molecular crowding and aggregation to be modeled. The biomolecules simulated can be represented as a sphere, or as a more complex geometry such as a domain structure or polymer chain. ReaDDy bridges the gap between small-scale but highly detailed molecular dynamics or Brownian dynamics simulations and large-scale but little-detailed reaction kinetics simulations. ReaDDy has a modular design that enables the exchange of the computing core by efficient platform-specific implementations or dynamical models that are different from Brownian dynamics.

https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0074261&type=printable

ReaDDy--a software for particle-based reaction-diffusion dynamics in crowded cellular environments.

We study a scheduling or allocation problem with the following characteristics: The goal is to execute a number of unspecified tasks on a parallel machine in any order and as quickly as possible. The tasks are maintained by a central monitor that will hand out batches of a variable number of tasks to requesting processors. A processor works on the batch assigned to it until it has completed all tasks in the batch, at which point it returns to the monitor for another batch. The time needed to execute a task is assumed to be a random variable with known mean and variance, and the execution times of distinct tasks are assumed to be independent. Moreover, each time a processor obtains a new batch from the monitor, it suffers a fixed delay. The challenge is to allocate batches to processors in such a way as to achieve a small expected overall finishing time. We introduce a new allocation strategy, the Bold strategy, and show that it outperforms other strategies suggested in the literature in a number of simulations.

Allocating Independent Tasks to Parallel Processors: An Experimental Study

Ensembles of widely distributed, heterogeneous resources, or Grids, have emerged as popular platforms for large-scale scientific applications. In this paper we present the Virtual Instrument project, which provides an integrated application execution environment that enables end-users to run and interact with running scientific simulations on Grids. This work is performed in the specific context of MCell, a computational biology application. While MCell provides the basis for running simulations, its capabilities are currently limited in terms of scale, ease-of-use, and interactivity. These limitations preclude usage scenarios that are critical for scientific advances. Our goal is to create a scientific "Virtual Instrument" from MCell by allowing its users to transparently access Grid resources while being able to steer running simulations. In this paper, we motivate the Virtual Instrument project and discuss a number of relevant issues and accomplishments in the area of Grid software development and application scheduling. We then describe our software design and report on the current implementation. We verify and evaluate our design via experiments with MCell on a real-world Grid testbed.

/pdf/the-virtual-instrument-support-for-grid-enabled-mcell-4za4g5omlt.pdf

The Virtual Instrument: Support for Grid-Enabled Mcell Simulations

The complexity of biological systems is enormous, even when considering a single cell where a multitude of highly par- allel and intertwined processes take place on the molec- ular level. This paper focuses on the parallel simulation of signal transduction processes within a cell carried out solely on the graphics processing unit (GPU). Each signaling molecule is represented by an agent performing a discrete- time continuous-space random walk to model its di usion through the cell. Since the interactions and reactions be- tween the agents can be competitive and are interdependent, we propose spatial partitioning for the reaction detection to overcome the data dependencies in the parallel execution of reactions. In addition, we present a simple way to simulate the Michaelis-Menten kinetics in our particle-based method using a per-particle delay. We apply this agent-based simu- lation to model signal transduction in the MAPK (Mitogen- Activated Protein Kinase) cascade both with and without cytoskeletal laments. Finally, we compare the speed-up of our GPU simulation with a parallelized CPU version result- ing in a twelvefold speedup.

Parallelized Agent-based Simulation on CPU and Graphics Hardware for Spatial and Stochastic Models in Biology Categories and Subject Descriptors

Most recent Grid middleware technologies have been aimed at the execution of sequential batch jobs. However, some users require interactive access when running jobs on Grid sites. Execution of these applications on a Grid environment is a challenging problem that requires the cooperation of several middleware tools and services. Additional problems arise when this interactive support is intended for parallel applications, which may run remotely across several sites. We provide transparent and reliable support for such applications. Our solution, based on the notion of split execution and interposition agents, allows running applications on a remote machine while some I/O operations are sent back to a home machine. The paper describes how we have applied interposition agents transparently to interactive applications and also describes a simple multi-programming mechanism that is used to start interactive applications as fast as possible even under heavy occupancy of resources. We provide a performance evaluation of the key elements involved in the execution of interactive jobs.

Resource Management for Interactive Jobs in a Grid Environment

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The Virtual Instrument: Support for Grid-Enabled Mcell Simulations