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

Neal ML, Carlson BE, Thompson CT, James RC, Kim KG, Tran K, Crampin EJ, Cook DL, Gennari JH - PLoS ONE (2015)

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.

ABSTRACT
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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Related in: MedlinePlus

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.
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pone.0145621.g003: 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.

Mentions: We manually incorporated these adjustments from the Terkildsen et al. PHN model into the simulation code of our SemGen-generated PHN model. Fig 3 compares the simulation results between this new version and the Terkildsen et al. version.


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

Neal ML, Carlson BE, Thompson CT, James RC, Kim KG, Tran K, Crampin EJ, Cook DL, Gennari JH - PLoS ONE (2015)

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.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4696653&req=5

pone.0145621.g003: 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.
Mentions: We manually incorporated these adjustments from the Terkildsen et al. PHN model into the simulation code of our SemGen-generated PHN model. Fig 3 compares the simulation results between this new version and the Terkildsen et al. version.

Bottom Line: 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 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, United States of America.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus