生物过程演算的随机模拟
Stochastic Simulation of Process Calculi for Biology
摘要：
生物系统通常涉及大量具有复杂、高度平行的相互作用和内在随机性的组件。为了模拟这种复杂性，已经开发了许多基于过程演算的程序设计语言，其中许多语言的表达能力足以生成无限数量的分子种类和反应。由于这种表现力，这种演算不能依赖于标准的基于反应的模拟方法，这需要固定数量的物种和反应。我们建议使用一个通用的抽象机器，可以实例化到一系列的过程演算和一系列基于反应的模拟算法，而不是为每个过程演算实现自定义的随机模拟算法。抽象机的功能就像一个即时编译器，它动态地更新可能的反应集，并在迭代周期中选择下一个反应。在这篇简短的论文中，我们对通用抽象机器进行了简要总结，并展示了如何使用称为吉莱斯皮直接方法的随机模拟算法来实例化它。我们还讨论了更广泛的影响，这样一个抽象的机器，并概述了它如何可以用来模拟多个结石同时在一个共同的框架。
英文摘要：
Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.
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