PEtab.jl

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PEtab.jl ​

PEtab.jl is a Julia package for creating parameter estimation problems to fit ordinary differential equation (ODE) and scientific machine learning (SciML) models to time-series data.

Main features ​

  • Define ODE parameter estimation problems directly in Julia, with models provided as Catalyst.jl ReactionSystem, ModelingToolkitBase.jl ODESystem, an OrdinaryDiffEq.jl ODEProblem, or as SBML model (imported via SBMLImporter.jl). Problems can be defined with a wide range of features, such as multiple observables and/or simulation conditions, events, and pre-equilibration (steady-state initialization).

  • Support for three types of scientific machine learning (SciML) problems combining mechanistic ODE models with machine-learning (ML) components: (1) ML in the ODE dynamics (e.g. UDEs/Neural ODEs), (2) ML in the observable/measurement model linking simulations to data, and (3) pre-simulation ML mapping high-dimensional inputs to ODE parameters.

  • Import and work with PEtab problems in both v1 and v2 of the PEtab format, as well as the PEtab-SciML standard format.

  • Built on the SciML ecosystem, with access to performant stiff and non-stiff ODE solvers from OrdinaryDiffEq.jl, and efficient gradients via forward-mode automatic differentiation (small models) and adjoint sensitivity analysis (large models).

  • High performant, often faster than the state-of-the-art toolbox AMICI by ~2× for gradient and parameter-estimation workloads [1].

  • High-level wrappers for parameter estimation via Optim.jl, Ipopt.jl, Fides.jl, and Optimization.jl.

  • Support for state-of-the-art SciML training strategies, such as curriculum learning and multiple shooting, via PEtabTraining.jl.

  • High-level wrapper for Bayesian inference via AdvancedHMC.jl (including NUTS) and AdaptiveMCMC.jl.

Star us on GitHub!

If you find the package useful in your work please consider giving us a star on GitHub. This will help us secure funding in the future to continue maintaining the package.

Latest news: PEtab.jl v5.0!

PEtab.jl v5.0 is a breaking release adding support for scientific machine learning (SciML) problems combining ODE and ML components. In addition, PEtab.jl has been updated to support Catalyst v16 and ModelingToolkitBase v1. For a full overview of changes, see the HISTORY file.

Installation ​

PEtab.jl is an officially registered Julia package, tested and supported on Linux, macOS and Windows. The easiest way to install it is via the Julia package manager. In the Julia REPL, enter:

julia
julia> ] add PEtab

or alternatively

julia
julia> using Pkg; Pkg.add("PEtab")

PEtab.jl is compatible with Julia 1.10 and above. For best performance, we strongly recommend using the latest Julia version, which can be most reliably installed using juliaup.

If you encounter installation issues, please consult the troubleshooting guide.

Getting help ​

If you have any problems using PEtab, here are some helpful tips:

  • Read the FAQ section in the online documentation.

  • Post your questions in the #sciml-sysbio channel on the Julia Slack. While PEtab.jl is not exclusively for systems biology, the #sciml-sysbio channel is where the package authors are most active.

  • If you encounter unexpected behavior or a bug, please see how to file an issue on the Contributing page.

Citation ​

If you use PEtab.jl in work that is published, please cite the paper below:

bibtex
@article{PEtabBioinformatics2025,
  title={PEtab.jl: advancing the efficiency and utility of dynamic modelling},
  author={Persson, Sebastian and Fr{\"o}hlich, Fabian and Grein, Stephan and Loman, Torkel and Ognissanti, Damiano and Hasselgren, Viktor and Hasenauer, Jan and Cvijovic, Marija},
  journal={Bioinformatics},
  volume={41},
  number={9},
  pages={btaf497},
  year={2025},
  publisher={Oxford University Press}
}