Physiome Model Repository
The Physiome Model Repository is powered by a loosely coupled collection of software tools and libraries, and has the following parts.
PMR2 software suite
The core of the Physiome Model Repository is powered by the PMR2 software suite. PMR2 is a set of libraries that are built upon Plone, an open source Content Management System (CMS), and with an integration package for Git, a Distributed Version Control System (DVCS) for the storage of model data. PMR2 is open source software and is tri-licensed under the terms of GPL, LGPL and MPL.
A formal description of how PMR2 is constructed is published under the following citation:
- Yu, T., Lloyd, C.M., Nickerson, D.P., Cooling, M.T., Miller, A.K., Garny, A., Terkildsen, J.R., Lawson, J., Britten, R.D., Hunter, P.J., Nielsen, P.M., The Physiome Model Repository 2. Bioinformatics, (2011) 27(5): 743-744 doi:10.1093/bioinformatics/btq723.
Third-party integration suites
Over time, a number of partner research groups have created various tools and libraries that became critical for supporting advanced usages of the model data within the repository. The following is a list of descriptions and citations of libraries and other research outputs that have been incorporated into the Physiome Model Repository over the years of its existence.
The core RICORDO research output was incorporated to form the semantic metadata indexing and search engine service for the Physiome Model Repository. In the background, PMR incorporated Virtuoso as the RDF Store, which handles the storage and management of RDF metadata that have been added to the repository. Users have the ability to select supported metadata files to be indexed into the RDF store. The searching is then implemented as a Python library for Plone, incorporating the core RDF query and inference rules provided by the RICORDO toolkit to achieve the search functionality.
- Wimalaratne, S.M., Grenon, P., Hoehndorf, R., Gkoutos, G.V., de Bono, B., An infrastructure for ontology-based information systems in biomedicine: RICORDO case study. Bioinformatics, (2012) 28(3): 448-450 doi:10.1093/bioinformatics/btr662.
While the Git clients have built-in tools for visualising changes to a model/workspace over the course of its development, it is a text-based system that has no awareness of the tree or graph nature of the modelling languages that the Physiome Project uses. We thus incorporated a software called BiVeS, which provides the visualisation of the differences between two model files in an interactive interface across the entirety of the repository, done without the restrictions of the standard text-based tools.
- Scharm, M., Wolkenhauer, O., Waltemath D., An algorithm to detect and communicate the differences in computational models describing biological systems. Bioinformatics, (2016) 32(4): 563-570. doi:10.1093/bioinformatics/btv484BiVeS.
- BiVeS project homepage
For the simulation and visualisation of CellML models, the desktop application OpenCOR is supported. Until the inclusion of the visualisation of simulation results are included directly into the PMR software suite, OpenCOR can be used and can easily be accessed via the "Launch with OpenCOR" links within all Exposure pages for CellML models within the repository.
A website for generating COMBINE archives, developed by the SEMS group at the University of Rostock. This is available under the Tools section of all exposure pages and can be used to generate COMBINE archives online.
- Scharm, M., Wendland, F., Peters, M., Wolfien, M., Theile, T., Waltemath, D., The CombineArchiveWeb application - A web based tool to handle files associated with modelling results. CEUR Workshop proceedings, Vol. 1320 (2014).
- WebCAT online
- WebCAT project homepage
An additional search engine developed by the SEMS group at the University of Rostock was incorporated as part of the full software suite. Morre is a front end to the MaSyMoS database system, and together they provide a ranking based search engine that will return more relevant models to end-users. Ranking of search results is based on CellML datatypes, semantic annotations and the model's underlying structure. This extra indexing feature is useful as it provides an additional method aiding model discovery beyond the strict semantic annotation indexes or plain text searching, especially in regard to models lacking semantic annotation.
- Henkel, R., Wolkenhauer, O., Waltemath, D., Combining computational models, semantic annotations and simulation experiments in a graph database. Database, (2015) 2015, bau130. doi:10.1093/database/bau130.