sbmlxdf 1.0.1

convert between SBML and tabular structures

sbmlxdf - convert between SBML and tabular structures

sbmlxdf is lightweight and transparent converter from SBML to pandas Dataframes (sbml2df) and from pandas Dataframes to SBML (df2sbml).

sbmlxdf supports, with few exceptions, all functionality of SBML L3V2 core package [1] and extension packages Flux Balance Constraints (fbc) [2], Groups (groups) [3] and Distributions (distrib) [4].

using libSBML API for accessing SBML [5].

Note: python-libsbml-experimental Python package is required only, when using features of SBML extension package distrib. python-libsbml Python package can be used in all other cases. python-libsbml and python-libsbml-experimental both share the same top level identifier libsbml (import libsbml). Conflicts can be resolved by pip uninstalling both packages and subsequntly installing the favoured package: e.g.

$ pip3 uninstall python-libsbml-experimental
$ pip3 uninstall python-libsbml
$ pip3 install python-libsbml-experimental

Benefits

kinetic modelers with and without programming skills

• overcome hesitation of creating own models in SBML
• have a tool for flexible kinetic modelling using spreadsheets
• inspect SBML models
• create/extend SBML models
• use latest SBML features
• generate ‘correct’ SBML models

Python programmers

• get access to SBML model data via pandas DataFrames, e.g. for use in their optimizers
• can evaluate different model design strategies

Features

• support of SBML L3V2 core [1], including:

  • model history, miriam-annotations, xml-annotations
  • units of measurement
  • local/global parameters
  • function definitions
  • Flux Balance Constraints package [2]
  • Groups package [3]
  • Distributions package [4]

sbmxdf does not intent to support SBML packages related to graphical representations of network models. I.e. packages Layout and Rendering are not supported. Other released SBML packages as of July 2021, see package status i.e. Hierarchical Model Composition, Multistate and Multicomponent Species and Qualitative Models are not supported at the moment, but might be included in future versions.

Small Python examples

(Note: Users without programming experience may use a command line interface to convert between SBML format and spreadsheet format)

1. Convert SBML model to spreadsheet
2. Check SBML compliance of spreadsheet model and convert to SBML
3. Access SBML model data

>>> import sbmlxdf
>>>
>>> model = sbmlxdf.Model('BIOMD0000000010_url.xml')
>>> model.to_excel('BIOMD0000000010_ulr.xlsx')

>>> import sbmlxdf
>>>
>>> upd_model = sbmlxdf.Model('BIO10_upd.xlsx')
>>> print('SBML validation result:', upd_model.validate_sbml('tmp.xml'))
>>> upd_model.export_sbml('BIO10_upd.xml')

>>> import sbmlxdf
>>>
>>> model = sbmlxdf.Model('BIO10_upd.xml')
>>> model_df = model.to_df()
>>> print(model_df.keys())
>>>
>>> df_r = model_df['reactions']
>>>
>>> print(len(df_r), 'reactions found, first reaction:' )
>>> print(df_r.iloc[0])
>>>
>>> for id, reaction in df_r.iterrows():
>>>   print('reaction:', id)
>>>   for record in sbmlxdf.extract_records(reaction['reactants']):
>>>      print('  reactant: ', sbmlxdf.extract_params(record))
>>>   for record in sbmlxdf.extract_records(reaction['products']):
>>>      print('  product:  ', sbmlxdf.extract_params(record))

Documentation

Introductory tutorials, how-to's and other useful documentation are available on Read the Docs

Installing

sbmlxdf is available on PyPI:

$ python -m pip install sbmlxdf

License

GPLv3

Peter Schubert, October 2020

References

[1]: The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core (Release 2) Authors: Michael Hucka, Frank T. Bergmann, Claudine Chaouiya, Andreas Dräger, Stefan Hoops, Sarah M. Keating, Matthias König, Nicolas Le Novère, Chris J. Myers, Brett G. Olivier, Sven Sahle, James C. Schaff, Rahuman Sheriff, Lucian P. Smith, Dagmar Waltemath, Darren J. Wilkinson, and Fengkai Zhang

[2]: Olivier, B. G., & Bergmann, F. T. (2018). SBML Level 3 Package: Flux Balance Constraints version 2. Journal of Integrative Bioinformatics, 15(1), 20170082.

[3]: Hucka, M., & Smith, L. P. (2016). SBML Level 3 package: Groups, Version 1 Release 1. Journal of Integrative Bioinformatics, 13(3), 290.

[4]: Smith, L. P., Moodie, S. L., Bergmann, F. T., Gillespie, C., Keating, S. M., König, M., Myers, C. J., Swat, M. J., Wilkinson, D.J., and Hucka, M. (2020). The Distributions Package for SBML Level 3. Retrieved from from COMBINE, https://identifiers.org/combine.specifications/ sbml.level-3.version-1.distrib.version-1.release-1