AUTHOR=Garny Alan , Hunter Peter J. 

TITLE=OpenCOR: a modular and interoperable approach to computational biology

JOURNAL=Frontiers in Physiology

VOLUME=6

YEAR=2015

URL=https://www.frontiersin.org/journals/physiology/articles/10.3389/fphys.2015.00026

DOI=10.3389/fphys.2015.00026

ISSN=1664-042X

ABSTRACT=<p>Computational biologists have been developing standards and formats for nearly two decades, with the aim of easing the description and exchange of experimental data, mathematical models, simulation experiments, etc. One of those efforts is CellML (<ext-link ext-link-type="uri" xlink:href="http://cellml.org" xmlns:xlink="http://www.w3.org/1999/xlink">cellml.org</ext-link>), an XML-based markup language for the encoding of mathematical models. Early CellML-based environments include COR and OpenCell. However, both of those tools have limitations and were eventually replaced with OpenCOR (<ext-link ext-link-type="uri" xlink:href="http://opencor.ws" xmlns:xlink="http://www.w3.org/1999/xlink">opencor.ws</ext-link>). OpenCOR is an open source modeling environment that is supported on Windows, Linux and OS X. It relies on a modular approach, which means that all of its features come in the form of plugins. Those plugins can be used to organize, edit, simulate and analyze models encoded in the CellML format. We start with an introduction to CellML and two of its early adopters, which limitations eventually led to the development of OpenCOR. We then go onto describing the general philosophy behind OpenCOR, as well as describing its openness and its development process. Next, we illustrate various aspects of OpenCOR, such as its user interface and some of the plugins that come bundled with it (e.g., its editing and simulation plugins). Finally, we discuss some of the advantages and limitations of OpenCOR before drawing some concluding remarks.</p>