Department of Computational Medicine, University of California, Los Angeles, CA, United States of America.
Department of Statistical Sciences, Duke University, Durham, NC, United States of America.
PLoS One. 2021 Mar 2;16(3):e0247046. doi: 10.1371/journal.pone.0247046. eCollection 2021.
Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source programming language Julia. We also apply our algorithms to several complex spatial stochastic phenomena. including a rock-paper-scissors game, cancer growth in response to immunotherapy, and lipid oxidation dynamics. Our approach aids in standardizing mathematical models and in generating hypotheses based on concrete mechanistic behavior across a wide range of observed spatial phenomena.
相互作用粒子系统 (IPS) 被用于模拟许多不同科学领域的时空随机系统。我们设计了一个算法框架，将 IPS 模拟简化为均匀混合化学反应网络 (CRN) 的模拟。该框架最小化了相关反应通道的数量，并将模拟的计算成本与晶格的大小解耦。解耦允许我们的软件利用通常为均匀混合 CRN 保留的广泛技术。我们在开源编程语言 Julia 中实现了直接随机模拟算法。我们还将我们的算法应用于几种复杂的空间随机现象，包括石头剪刀布游戏、免疫治疗反应中的癌症生长和脂质氧化动力学。我们的方法有助于标准化数学模型，并根据广泛观察到的空间现象中的具体机械行为生成假设。
