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PLoS One. 2015 Dec 30;10(12):e0145621. doi: 10.1371/journal.pone.0145621. eCollection 2015.

Semantics-Based Composition of Integrated Cardiomyocyte Models Motivated by Real-World Use Cases.

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Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.
Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, United States of America.
Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Victoria, Australia.
ARC Centre of Excellence in Convergent Bio-Nano Science and Technology, Melbourne School of Engineering, University of Melbourne, Victoria, Australia.
School of Mathematics and Statistics, University of Melbourne, Victoria, Australia.
School of Medicine, University of Melbourne, Victoria, Australia.
Department of Physiology and Biophysics, University of Washington, Seattle, WA, United States of America.


Semantics-based model composition is an approach for generating complex biosimulation models from existing components that relies on capturing the biological meaning of model elements in a machine-readable fashion. This approach allows the user to work at the biological rather than computational level of abstraction and helps minimize the amount of manual effort required for model composition. To support this compositional approach, we have developed the SemGen software, and here report on SemGen's semantics-based merging capabilities using real-world modeling use cases. We successfully reproduced a large, manually-encoded, multi-model merge: the "Pandit-Hinch-Niederer" (PHN) cardiomyocyte excitation-contraction model, previously developed using CellML. We describe our approach for annotating the three component models used in the PHN composition and for merging them at the biological level of abstraction within SemGen. We demonstrate that we were able to reproduce the original PHN model results in a semi-automated, semantics-based fashion and also rapidly generate a second, novel cardiomyocyte model composed using an alternative, independently-developed tension generation component. We discuss the time-saving features of our compositional approach in the context of these merging exercises, the limitations we encountered, and potential solutions for enhancing the approach.

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