{"title":"SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem.","authors":"Paul F Lang, Anand Jain, Christopher Rackauckas","doi":"10.1515/jib-2024-0003","DOIUrl":null,"url":null,"abstract":"<p><p>Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.</p>","PeriodicalId":53625,"journal":{"name":"Journal of Integrative Bioinformatics","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11294517/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Integrative Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1515/jib-2024-0003","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/3/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Julia is a general purpose programming language that was designed for simplifying and accelerating numerical analysis and computational science. In particular the Scientific Machine Learning (SciML) ecosystem of Julia packages includes frameworks for high-performance symbolic-numeric computations. It allows users to automatically enhance high-level descriptions of their models with symbolic preprocessing and automatic sparsification and parallelization of computations. This enables performant solution of differential equations, efficient parameter estimation and methodologies for automated model discovery with neural differential equations and sparse identification of nonlinear dynamics. To give the systems biology community easy access to SciML, we developed SBMLToolkit.jl. SBMLToolkit.jl imports dynamic SBML models into the SciML ecosystem to accelerate model simulation and fitting of kinetic parameters. By providing computational systems biologists with easy access to the open-source Julia ecosystevnm, we hope to catalyze the development of further Julia tools in this domain and the growth of the Julia bioscience community. SBMLToolkit.jl is freely available under the MIT license. The source code is available at https://github.com/SciML/SBMLToolkit.jl.
Julia 是一种通用编程语言,旨在简化和加速数值分析和计算科学。特别是 Julia 软件包的科学机器学习(SciML)生态系统包括高性能符号数值计算框架。它允许用户通过符号预处理、自动稀疏化和并行化计算,自动增强模型的高级描述。这样就能实现微分方程的高效求解、高效参数估计以及利用神经微分方程和非线性动力学稀疏识别自动发现模型的方法。为了让系统生物学界能方便地使用 SciML,我们开发了 SBMLToolkit.jl。SBMLToolkit.jl 将动态 SBML 模型导入 SciML 生态系统,以加速模型模拟和动力学参数拟合。我们希望通过为计算系统生物学家提供对开源 Julia 生态系统的便捷访问,促进该领域更多 Julia 工具的开发和 Julia 生物科学社区的发展。SBMLToolkit.jl 在 MIT 许可下免费提供。源代码可从 https://github.com/SciML/SBMLToolkit.jl 获取。