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High-throughput functional curation of cellular electrophysiology models
Jonathan Cooper1, Gary R Mirams, Steven A Niederer
1Oxford University Computing Laboratory, University of Oxford, Wolfson Building, Parks Road, Oxford OX13QD, UK. jonathan.cooper@comlab.ox.ac.uk
Progress in Biophysics and Molecular Biology
|June 28, 2011
Summary
Understanding mathematical model function is key for reuse. A new simulation environment evaluates model behavior, revealing significant variations and improving model selection for cardiac electrophysiology research.
Area of Science:
- Computational Biology
- Mathematical Modeling
- Cardiac Electrophysiology
Background:
- Effective reuse of quantitative mathematical models necessitates understanding their functional capabilities and application scope beyond just equations.
- Existing model repositories often lack functional characterization, hindering reliable application in new research contexts.
Purpose of the Study:
- To develop and demonstrate a simulation environment for high-throughput functional curation of mathematical models.
- To evaluate the functional response and variability of cardiac electrophysiology cell models.
Main Methods:
- Developed a simulation environment for evaluating mathematical model functional response to user-defined protocols.
- Applied the environment to 31 cardiac electrophysiology cell models.
- Assessed models using S1-S2 response for restitution curves and L-type calcium channel current-voltage relationships.
Main Results:
- Demonstrated significant variation in functional responses among cardiac cell models, even for those from the same species and temperature.
- Highlighted the importance of functional characterization for model reuse.
- Identified specific model behaviors related to restitution and ion channel function.
Conclusions:
- The developed simulation environment enables effective functional curation of mathematical models.
- Understanding model functional characteristics is crucial for accurate and robust model reuse in scientific research.
- This approach facilitates better model selection, identification of models exhibiting specific phenomena, and robust incremental model development.
