At the same time as we developed OneModel, we created SBML2dae: a SBML-compatible python package which provides programming tools to generate SBML exporters to other programming languages for simulation or analysis. SBML2dae is open-source and complies with OneModel's design philosophy.
By default, SBML2dae only allows exporting SBML to Matlab. However, it is straightforward for an expert user to create a new parser for another programming language such as Modelica, Julia, or Python. We expect more syntactic parsers to be incorporated using SBML2dae (by our group or by the community).
The differences with other Matlab parsers are (i) SBML2dae allows the simulation of algebraic loops (an indispensable element for the simulation of reduced-order models, using the quasi-steady-state approximation), (ii) it generates Matlab code using classes that significantly facilitates the integration of the models with the rest of Matlab tools and (iii) SBML2dae is easily modifiable to change the way of exporting the models.
There are excellent tools for simulation and analysis of SBML models, but one of the most significant drawbacks is when the tool does not fit the needs of pre-existing workflows. SBML2dae solves this problem by allowing the user to implement customized SBML parsers that fit their particular workflow quickly.
SBML2dae can be installed from PyPI and is compatible with Python 3.7+ for Window, Mac and Linux.
The latest package can be installed with:
pip install sbml2daeSBML2dae provides a simple command-line interface to export SBML models into Matlab code.
Go to the examples folder in this repository and use the following command:
sbml2dae export ex01_simple_gene_expression.xml
It will generate a build folder with the SBML model exported into Matlab.
If you use SBML2dae in your research, please use the following citations in your published works:
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Santos-Navarro, F. N., Navarro, J. L., Boada, Y., Vignoni, A., & Picó, J. (2022). "OneModel: an open-source SBML modeling tool focused on accessibility, simplicity, and modularity." DYCOPS.
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Santos-Navarro, F. N., Vignoni, A., & Picó, J. (2022). "Multi-scale host-aware modeling for analysis and tuning of synthetic gene circuits for bioproduction." PhD thesis.
Copyright 2022 Fernando N. Santos-Navarro, Jose Luis Herrero, Yadira Boada, Alejandro Vignoni, and Jesús Picó
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