SemGen is an experimental software tool for automating the modular composition and decomposition of biosimulation models.
SemGen facilitates the construction of complex, integrated models, and the swift extraction of reusable sub-models from larger ones. SemGen relies on the semantically-rich SemSim model description format to help automate these modeling tasks.
With SemGen, users can
Visualize models using D3 force-directed networks
Create SemSim versions of existing models and annotate them with rich semantic data
Automatically decompose models into interoperable sub-models
Semi-automatically merge models into more complex systems
Encode models in executable simulation formats
This software is experimental and we encourage feedback, bug reports, etc. from all users. We intend to improve SemGen through user-based development, so feel free to contact us directly if you are interested in using and improving SemGen.
Dr. Maxwell Neal originally developed the SemGen software as part of his dissertation research. Currently, Dr. Neal leads a team of developers to further augment, test and evaluate SemGen under an R01 grant from the National Library of Medicine (PIs: John Gennari and Brian Carlson) that aims to accelerate model-driven research. Contributors to SemGen development include Christopher Thompson, Graham Kim and Ryan James.
How to cite SemGen:
SemGen has been developed with support from the American Heart Association, the European Commission, and the United States National Institutes of Health.
Screenshots (version 4.0.0)
Mathematical dependency network of a hemodynamics model.
Coupling points between the hemodynamics model and a baroreceptor model automatically identified by SemGen during a merging task (blue lines).