SBMLToolkit.jl: a Julia package for importing SBML into the SciML ecosystem.

Area of Science:

• Computational Biology
• Scientific Machine Learning
• Systems Biology

Background:

• Julia is a high-performance programming language designed for numerical analysis and computational science.
• The Scientific Machine Learning (SciML) ecosystem in Julia offers tools for symbolic-numeric computations, enabling automated model enhancement, parallelization, and efficient solution of differential equations.
• Automated model discovery, parameter estimation, and identification of nonlinear dynamics are key applications within SciML.

Purpose of the Study:

• To provide the systems biology community with seamless access to the SciML ecosystem.
• To develop SBMLToolkit.jl for importing dynamic Systems Biology Markup Language (SBML) models into Julia.
• To accelerate model simulation and kinetic parameter fitting for biological systems.

Main Methods:

• Development of SBMLToolkit.jl, a Julia package.
• Importing dynamic SBML models into the SciML framework.
• Leveraging Julia's high-performance computing capabilities for biological model analysis.

Main Results:

• SBMLToolkit.jl successfully imports SBML models into the SciML ecosystem.
• The toolkit facilitates accelerated model simulation and kinetic parameter estimation.
• It enables computational systems biologists to utilize advanced SciML tools.

Conclusions:

• SBMLToolkit.jl lowers the barrier for systems biologists to use Julia for advanced computational modeling.
• The toolkit is expected to foster the development of new Julia-based bioscience tools.
• It aims to grow the Julia bioscience community by providing accessible, high-performance computational resources.