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Using CellML in computational models of multiscale physiology.

David Nickerson1, Peter Hunter

  • 1Bioengineering Institute, The University of Auckland, Auckland, New Zealand.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|February 7, 2007
PubMed
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A new computational framework integrates multiple physics and spatial scales for physiological models. It utilizes CellML for mathematical equations, demonstrating applications in cardiac electromechanics across cellular, tissue, and organ levels.

Area of Science:

  • Computational biology
  • Multiphysics modeling
  • Physiological systems simulation

Background:

  • Integrating diverse physics and spatial scales in physiological models is challenging.
  • Existing frameworks may lack flexibility in specifying complex mathematical relationships.

Purpose of the Study:

  • To present a computational modeling framework enabling the integration of multiple physics and spatial scales.
  • To demonstrate the framework's utility using cardiac electromechanics models.

Main Methods:

  • Developed a computational framework for integrating multiphysics and multiscale models.
  • Employed CellML to define all model and simulation-specific mathematical equations.
  • Applied the framework to cardiac electromechanics at cellular, tissue, and organ scales.

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Main Results:

  • Successfully integrated multiple physics and spatial scales within the framework.
  • Demonstrated the framework's capability through detailed cardiac electromechanics simulations.
  • Validated the use of CellML for specifying complex cellular and constitutive equations.

Conclusions:

  • The presented framework effectively integrates diverse physical phenomena and spatial scales in physiological modeling.
  • CellML provides a robust method for defining mathematical components in complex simulations.
  • The framework has broad applicability for various physiological system modeling challenges.