SEDML_SBML

Generation of SED-ML files

SED-ML

Simulation Experiment Description Markup Language (SED-ML) was developed as a community project starting in 2007 and was initially published in 2011 (Source). SED-ML represents a computer-readable, XML-based exchange format enabling the validation and reproduction of simulation experiments [1].

SED-ML documents contain five major elements:

1. Models: reference to model used in the experiment
2. Simulation: definition of simulation settings and algorithms e.g. KiSAO
3. Tasks: application of a simulation algorithm to a model
4. Data Generators: post-processing of results
5. Output: reports or plots based on the data from the data generators

Further information regarding the specifications of the latest release L1V3 can be found on the SED-ML website.

Creation of SED-ML files

Initially, we decided to follow the steps taken by Scharm & Waltemath [2] to generate a default simulation using SED-ML WebTools v2.4 and modify this for a specific experiment in COPASI 4.33. This approach is documented in more detail here.

COPASI's support for SED-ML files, however, is limited to simulations using only one model. It is often necessary though to combine different model setups in one simulation, e.g. to compare a wildtype and overexpression condition.

We therefore generated experiment-specific SED-ML files using an alternative tool, Tellurium v2.2.0, a python-based environment to build, analyse, simulate and reproduce biological models [3]. This approach is documented in more detail here.

References

[1] Waltemath, D. et al. Reproducible computational biology experiments with SED-ML--the Simulation Experiment Description Markup Language. BMC Syst Biol 5, 198 (2011), https://doi.org/10.1186/1752-0509-5-198

[2] Scharm, M. and Waltemath, D. A fully featured COMBINE archive of a simulation study on syncytial mitotic cycles in Drosophila embryos. F1000Research 5, 2421 (2016). https://doi.org/10.12688/f1000research.9379.1

[3] Choi, K. et al. Tellurium: An extensible python-based modeling environment for systems and synthetic biology. Biosystems 171, 74-79 (2018), https://doi.org/10.1101/054601