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Bachmann_Model_Quelle
A search to check for available Bachmann model was performed (see below).
We found in our research five SBML-Bachmann models, one of them as support information of Bachmann et. al ([1]), this is the first delivered model. The others come from two different repositories, JWS Online and BioModels.
We found two models on the repository of BioModels. The first, BIOMD0000000347_url.xml, was submitted on 22nd July 2011 and modified on 31st January 2012. Together with this model were other files in different formats. Most of them were generated by tools to simulate, visualize, validate and document the model. The second and newest, Bachmann2011.xml, was posted on 14th November 2019. This one hat other complementary files for the simulation of this model.
The models in JWS Online do not have any date of building or update, so we do not know when these were built. The first model in JWS online, bachmann.xml. This model is from Mus musculus and represents the STAT's pathway in a cell simulation in silico. The model bachmann2.xml was obtained from the BioModels database (BioModels ID: BIOMD0000000347).
[1] Bachmann, J., Raue, A., Schilling, M. et al. Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Molecular Systems Biology 7, 516 (2011). https://doi.org/10.1038/msb.2011.50
Bioinformatics & Systems biology SS 2021
- Synopsis Group 1
- Sources of Bachmann model
- Software tools for simulation
- How to build a Fully Featured COMBINE Archive?
- Communication channels
- Provision of a template for documentation
- Schedule (draft)
- Review of results
- COMBINE Archive (Testversion!)
- Synopsis Group 2
- Finding of SBML models
- Comparison of SBML models
- The chosen one
- Simulation tools
- Metadata
- Improving metadata annotations
- Synopsis Group 3
- SBGN Maps for Bachmann model
- Choice of SBGN language
- Tool to draw the SBGN Map
- SBGN-Map Drawing, Validation & Beautification
- Integration into COMBINE Archive
- Synopsis Group 4
- Selection of experiments
- Selection of SED-ML tool(s)
- Generation of SED-ML file(s)
- Integration into COMBINE Archive
- Test of SED-ML files and COMBINE Archive