Create parameter estimation problems for dynamic models

PEtab.jl is a Julia package for creating parameter estimation problems for fitting Ordinary Differential Equation (ODE) models to data in Julia. Some major highlights of PEtab.jl are:

• It supports coding parameter estimation problems directly in Julia, where the dynamic model can be provided as a Catalyst ReactionSystem, a ModelingToolkit ODESystem, or as an SBML file imported through SBMLImporter.
• It can import and has full support for parameter estimation problems in the PEtab standard format
• It supports a wide range of features for parameter estimation problems, including multiple observables, multiple simulation conditions, models with events, and models with steady-state pre-equilibration simulations.
• It integrates with Julia's DifferentialEquations.jl ecosystem, which among other things, means it supports any of the state-of-the-art ODE solvers in OrdinaryDiffEq.jl.
• It supports efficient forward and adjoint gradient methods, suitable for small and large models, respectively.
• It supports exact Hessian's for small models and good approximations for large models.
• It includes wrappers for performing parameter estimation with optimization packages Optim.jl, Ipopt, Optimization.jl, and Fides.py.
• It includes wrappers for performing Bayesian inference using state-of-the-art methods such as NUTS (the same sampler used in Turing.jl) or AdaptiveMCMC.jl.

Additional information and tutorials can be found in the documentation.

PEtab.jl is a registered Julia package and can be installed with the Julia package manager using:

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

PEtab.jl is compatible with Julia 1.10 and above. For additional installation details, see the documentation.

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

@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}
}