Year: 2021
Type: article
Abstract: 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.
Source: PLoS ONE
Institutions: University of California, Los Angeles, Duke University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,749
Funders: National Science Foundation, Susan G. Komen, National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences, Clinical and Translational Science Institute, University of California, Los Angeles
Awards: PostDoctoral Research Fellowship, G2825077807, G3541040518, G447312286, G5096767427, Statistical Methods for Gene Mapping, G7159390087, Genomics, EHRs, GPUs, and Next Generation Computational Statistics, UCLA Clinical and Translational Science Institute, G9000314549, Training Grant in Genomic Analysis and Interpretation
OpenAlex ID: W3134328146