{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/biosimulator-jl-stochastic-simulation-in","title":"BioSimulator.jl: Stochastic simulation in Julia","arxiv_id":"1811.12499","date":"2018-11-29","proceeding":null,"authors":[],"abstract":"Biological systems with intertwined feedback loops pose a challenge to\nmathematical modeling efforts. Moreover, rare events, such as mutation and\nextinction, complicate system dynamics. Stochastic simulation algorithms are\nuseful in generating time-evolution trajectories for these systems because they\ncan adequately capture the influence of random fluctuations and quantify rare\nevents. We present a simple and flexible package, BioSimulator.jl, for\nimplementing the Gillespie algorithm, $\\tau$-leaping, and related stochastic\nsimulation algorithms. The objective of this work is to provide scientists\nacross domains with fast, user-friendly simulation tools. We used the\nhigh-performance programming language Julia because of its emphasis on\nscientific computing. Our software package implements a suite of stochastic\nsimulation algorithms based on Markov chain theory. We provide the ability to\n(a) diagram Petri Nets describing interactions, (b) plot average trajectories\nand attached standard deviations of each participating species over time, and\n(c) generate frequency distributions of each species at a specified time.\nBioSimulator.jl's interface allows users to build models programmatically\nwithin Julia. A model is then passed to the simulate routine to generate\nsimulation data. The built-in tools allow one to visualize results and compute\nsummary statistics. Our examples highlight the broad applicability of our\nsoftware to systems of varying complexity from ecology, systems biology,\nchemistry, and genetics. The user-friendly nature of BioSimulator.jl encourages\nthe use of stochastic simulation, minimizes tedious programming efforts, and\nreduces errors during model specification.","url_abs":"http://arxiv.org/abs/1811.12499v1","url_pdf":"http://arxiv.org/pdf/1811.12499v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"biosimulator-jl-stochastic-simulation-in","repo_url":"https://github.com/alanderos91/BioSimulator.jl","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}