论文标题
随机模拟算法的基于扩散的嵌入在连续空间中
A Diffusion-Based Embedding of the Stochastic Simulation Algorithm in Continuous Space
论文作者
论文摘要
已经使用了多种模拟方法来建模反应扩散动力学 - 包括基于微分方程（DE）的方法，随机模拟算法（SSA），Brownian Dynamics（BD），Green的功能反应动力学（GFRD），并在其上进行差异 - 可以使他们的稳定范围构成范围，以实验范围，以实验范围。测量。在这里，我们开发了一种多尺度方法，该方法结合了适用于混合系统的有效SSA样采样和较慢但空间感知的GFRD模型的各个方面，假设与GFRD一样，反应在空间异质环境中发生，必须显式建模。我们的方法以两种主要方式扩展了SSA方法。首先，我们在扩散运动后采样双分子缔合反应，并具有时间依赖性反应倾向。其次，反应位置是从重叠扩散球内进行采样的，描述了单个反应物的空间概率密度。我们展示了通过应用于Michaelis-Menten模型的替代方法进行有效仿真对空间异质生物化学的有效模拟的方法。
A variety of simulation methodologies have been used for modeling reaction-diffusion dynamics -- including approaches based on Differential Equations (DE), the Stochastic Simulation Algorithm (SSA), Brownian Dynamics (BD), Green's Function Reaction Dynamics (GFRD), and variations thereon -- each offering trade-offs with respect to the ranges of phenomena they can model, their computational tractability, and the difficulty of fitting them to experimental measurements. Here, we develop a multiscale approach combining efficient SSA-like sampling suitable for well-mixed systems with aspects of the slower but space-aware GFRD model, assuming as with GFRD that reactions occur in a spatially heterogeneous environment that must be explicitly modeled. Our method extends the SSA approach in two major ways. First, we sample bimolecular association reactions following diffusive motion with a time-dependent reaction propensity. Second, reaction locations are sampled from within overlapping diffusion spheres describing the spatial probability densities of individual reactants. We show the approach to provide efficient simulation of spatially heterogeneous biochemistry in comparison to alternative methods via application to a Michaelis-Menten model.
