A simple CLI and library for BioNetGen modelling language
A simple CLI for BioNetGen
This is a simple CLI and a library for BioNetGen modelling language. PyBioNetGen also includes a heavily updated version of Atomizer which allows for conversion of models written in Systems Biology Markup Language (SBML) into BioNetGen language (BNGL) format.
Please see the documentation to learn how to use PyBioNetGen.
You will need both python (3.7 and above) and perl installed. Once both are available you can use the following pip command to install the package
$ pip install bionetgen
PyBioNetGen comes with a command line interface (CLI), based on cement framework, as well as a functional library that can be imported. The CLI can be used to run BNGL models, generate Jupyter notebooks and do rudimentary plotting.
The library side provides a simple BNGL model runner as well as a model object that can be manipulated and used to get libRoadRunner simulators for the model.
PyBioNetGen also includes a heavily updated version of Atomizer which allows for conversion of SBML models into BNGL format. Atomizer can also be used to automatically try to infer the internal structure of SBML species during the conversion, see here for more information. Please note that this version of Atomizer is the main supported version and the version distributed with BioNetGen will eventually be deprecated.
The model object requires a system call to BioNetGen so the initialization can be relatively costly, in case you would like to use it for parallel applications, use the libRoadRunner simulator instead, unless you are doing NFSim simulations.
Sample CLI usage
$ bionetgen -h # help on every subcommand $ bionetgen run -h # help on run subcommand $ bionetgen run -i mymodel.bngl -o output_folder # this runs the model in output_folder
Sample library usage
import bionetgen ret = bionetgen.run("/path/to/mymodel.bngl", out="/path/to/output/folder") # out keyword is optional, if not given, # generated files will be deleted after running res = ret.results['mymodel'] # res will be a numpy record array of your gdat results model = bionetgen.bngmodel("/path/to/mymodel.bngl") # model will be a python object that contains all model information print(model.parameters) # this will print only the parameter block in BNGL format print(model) # this will print the entire BNGL model.parameters.k = 1 # setting parameter k to 1 with open("new_model.bngl", "w") as f: f.write(str(model)) # writes the changed model to new_model file # this will give you a libRoadRunner instance of the model librr_sim = model.setup_simulator()
You can find more tutorials here.
The following demonstrates setting up and working with a development environment:
### create a virtualenv for development $ make virtualenv $ source env/bin/activate ### run bionetgen cli application $ bionetgen --help ### run pytest / coverage $ make test
Included is a basic
Dockerfile for building and distributing
and can be built with the included
$ make docker $ docker run -it bionetgen --help
Publishing to PyPI
You can use
make dist command to make the distribution and push to PyPI with
python -m twine upload dist/*
You'll need to have a PyPI API token created, see here for more information.
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