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
[Click here for more background on SemGen and a detailed software guide]
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.
SemGen development is currently supported by a grant from the National Library of Medicine and through the Virtual Physiological Rat project.
Screenshots
Mathematical dependency network of a model that simulates myocyte tension development as visualized using the Stage tool (available in version 3.0 and higher).
The semantic annotations for codeword "VLV" from a cardiovascular dynamics model as displayed by the Annotator tool.
Extractor: Preview of an extraction of the left ventricular component of a cardiovascular dynamics model.
Clusters within the computational dependency network of the cardiovascular dynamics model as visualized by the Extractor's clustering tool.
Coupling points between the cardiovascular dynamics model and a baroreceptor model as identified by the Merger tool.
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DOWNLOAD SemGen 3.0.5 Requires Java 1.7 or higher
 MAC OS X
Documentation
SemGen help file
SemGen license
How to cite SemGen
Gennari, J.H., M.L. Neal, M. Galdzicki, and D.L. Cook. Multiple ontologies in action: composite annotations for biosimulation models. Journal of Biomedical Informatics, 2011. 44(1):146-154.
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