Lang Paul F, Jain Anand, Rackauckas Christopher
Deep Origin, South San Francisco, USA.
JuliaHub, Boston, USA.
J Integr Bioinform. 2024 May 28;21(1). doi: 10.1515/jib-2024-0003. eCollection 2024 Mar 1.
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与计算科学。特别是，Julia包的科学机器学习（SciML）生态系统包括用于高性能符号数值计算的框架。它允许用户通过符号预处理以及计算的自动稀疏化和并行化，自动增强其模型的高级描述。这使得能够高效地求解微分方程、进行有效的参数估计，并采用神经微分方程和非线性动力学的稀疏识别方法进行自动模型发现。为了让系统生物学界能够轻松使用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。通过为计算系统生物学家提供轻松访问开源Julia生态系统的途径，我们希望推动该领域更多Julia工具的开发以及Julia生物科学界的发展。SBMLToolkit.jl在MIT许可下可免费获取。源代码可在https://github.com/SciML/SBMLToolkit.jl获取。
