{"id":17377331,"url":"https://github.com/paulflang/sbml2julia","last_synced_at":"2026-02-28T19:32:22.626Z","repository":{"id":43332584,"uuid":"260534848","full_name":"paulflang/SBML2Julia","owner":"paulflang","description":"A tool to for optimizing parameters of ordinary differential equation (ODE) models. 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For importing SBML models into the the SciML ecosystem, please refer to [SBMLToolkit.jl](https://github.com/SciML/SBMLToolkit.jl).\n\n## Optimization method\n\n`SBML2Julia` uses the optimization method presented in [Scalable nonlinear programming framework for parameter estimation in dynamic biological system models](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006828). In brief, contrary to typical parameter optimization methods for ODE systems, `SBML2Julia` does not rely on simulation of the ODE system. Instead `SBML2Julia` uses an implicit Euler scheme to time-discretize an ODE system of n equations into m time steps. This transforms the ODE system into a system of n * (m - 1) algebraic equations with n * m variables. These n * m variables (or a subset thereof) can then be cast into an objective function. `SBML2Julia` then uses interior-point optimization implemented in the Julia language to minimize the objective function constraint to the n * (m - 1) algebraic equations.\n\n## Installation\n\n`SBML2Julia` depends on several Python and Julia packages. If you have Docker installed on your machine, the easiest way of installing these dependencies is to pull the latest [SBML2Julia docker image](https://hub.docker.com/repository/docker/paulflang/sbml2julia) from Docker Hub and build a container.\n  ```\n  user@bash:/$ docker pull paulflang/sbml2julia:latest\n  user@bash:/$ docker run -it --mount type=bind,source=\u003cmy_host_dir\u003e,target=/media paulflang/sbml2julia:latest\n  ```\nTo install the latest `SBML2Julia` release in the Docker container, run:\n  ```\n  user@bash:/$ python3 -m pip install sbml2julia\n  ```\nAlternatively, to install the latest `SBML2Julia` version from GitHub, run:\n  ```\n  user@bash:/$ git clone https://github.com/paulflang/sbml2julia.git\n  user@bash:/$ python3 -m pip install sbml2julia\n  ```\nTo check if the installation was succesful, run:\n  ```\n  user@bash:/$ sbml2julia -h\n  ```\nIf you do not want to use Docker, the `SBML2Julia` dependencies can be installed as indicated in the [Dockerfile](https://github.com/paulflang/sbml2julia/blob/master/Dockerfile). Once these dependencie are installed, `SBML2Julia` can be installed as above.\n\n## Interfaces\n\nOptimization tasks can be performed from a Python API or a command line interface.\n\n## Tutorial, and documentation\nPlease see the [documentation](https://sbml2julia.readthedocs.io/en/latest/index.html) for a description of how to use `SBML2Julia`. \n\n## License\nThe package is released under the [MIT license](https://github.com/paulflang/SBML2Julia/blob/develop/LICENSE).\n\n## Development team\nThis package was developed by [Paul F. Lang](https://www.linkedin.com/in/paul-lang-7b54a81a3/) at the University of Oxford, UK and [Sungho Shin](https://www.sunghoshin.com/) at the University of Wisconsin-Madison, USA..\n\n\n## Questions and comments\nPlease contact [Paul F. Lang](mailto:paul.lang@wolfson.ox.ac.uk) with any questions or comments.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaulflang%2Fsbml2julia","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fpaulflang%2Fsbml2julia","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fpaulflang%2Fsbml2julia/lists"}