Open AccessStochastic simulation algorithms for Interacting Particle Systems
Author(s) -
Timothy C. Stutz,
Alfonso Landeros,
Jason Xu,
Janet S. Sinsheimer,
Mary E. Sehl,
Kenneth Lange
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0247046
Subject(s) - computer science , decoupling (probability) , theoretical computer science , granularity , range (aeronautics) , software , stochastic simulation , particle system , algorithm , mathematical optimization , distributed computing , programming language , mathematics , control engineering , engineering , composite material , operating system , statistics , materials science
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.