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. 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

Free PMC article

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

Maxwell L Neal et al. PLoS One. .
Free PMC article


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.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Fig 1
Fig 1. Architecture of the integrated PHN model.
Coloring indicates a component’s source model. Ion flows without circles represent facilitated transport; those with circles represent active transport. Adapted from Fig 1 of Terkildsen et al. [9].
Fig 2
Fig 2. Comparison between simulation results from the original PHN model and the SemGen-generated version.
Fig 3
Fig 3. Comparison between simulation results from the original PHN model and the manually-modified SemGen-generated version.
This modified SemGen-generated model includes the adjustments to equations and initial conditions that were introduced into the PHN model published by Terkildsen et al.
Fig 4
Fig 4. Simulation results for the PHT model demonstrating coupling of the stimulation current, membrane voltage, calcium transients, and cardiomyocyte force generation.

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