ArticleComputer methods and programs in biomedicine2018
BioSimulator.jl: Stochastic simulation in Julia.
Article in Computer methods and programs in biomedicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- GillesPy2: A Biochemical Modeling Framework for Simulation Driven Biological Discovery.Letters in biomathematics · 2023Article
- Differential methods for assessing sensitivity in biological models.PLoS computational biology · 2022Article
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- Stochastic activation and bistability in a Rab GTPase regulatory network.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
BACKGROUND AND
objectivesBiological systems with intertwined feedback loops pose a challenge to mathematical modeling efforts. Moreover, rare events, such as mutation and extinction, complicate system dynamics. Stochastic simulation algorithms are useful in generating time-evolution trajectories for these systems because they can adequately capture the influence of random fluctuations and quantify rare events. We present a simple and flexible package, BioSimulator.jl, for implementing the Gillespie algorithm, τ-leaping, and related stochastic simulation algorithms. The objective of this work is to provide scientists across domains with fast, user-friendly simulation tools.
methodsWe used the high-performance programming language Julia because of its emphasis on scientific computing. Our software package implements a suite of stochastic simulation algorithms based on Markov chain theory. We provide the ability to (a) diagram Petri Nets describing interactions, (b) plot average trajectories and attached standard deviations of each participating species over time, and (c) generate frequency distributions of each species at a specified time.
resultsBioSimulator.jl's interface allows users to build models programmatically within Julia. A model is then passed to the simulate routine to generate simulation data. The built-in tools allow one to visualize results and compute summary statistics. Our examples highlight the broad applicability of our software to systems of varying complexity from ecology, systems biology, chemistry, and genetics.
conclusionThe user-friendly nature of BioSimulator.jl encourages the use of stochastic simulation, minimizes tedious programming efforts, and reduces errors during model specification.
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