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Peter Schubert, Prof. Martin Lercher and the team of the Institute of Computational Cell Biology, Heinrich-Heine-University Duesseldorf, Germany

Welcome to sbmlxdf’s documentation!

Convert between SBML and tabular structures

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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 [SBML_L3V2] and packages Flux Balance Constraints (fbc) [SBML_fbc], Groups (groups) [SBML_groups] and Distributions (distrib) [SBML_distrib].

At the backend, libSBML API is used for accessing SBML data elements [libsbml].

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 [SBML_L3V2], including:

    • model history, miriamAnnotations, xmlAnnotations

    • units of measurement

    • local/global parameters

    • function definitions

    • Flux Balance Constraints package [SBML_fbc]

    • Groups package [SBML_groups]

    • Distributions package [SBML_distrib]

Note

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.