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Code generator for distributed parameter biological model simulation with PDE numerical schemes.

Florencio Rusty Punzalan, Yoshiharu Yamashita, Masanari Kawabata

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 11, 2013
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    Summary
    This summary is machine-generated.

    This study introduces a novel replacement scheme for discretizing partial differential equations (PDEs) in physiological simulations. This computational method simplifies complex models, enabling generalizable code generation for tissue and organ simulations.

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    Area of Science:

    • Computational Biology
    • Biophysics
    • Scientific Computing

    Background:

    • Physiological simulations at tissue and organ levels often rely on complex partial differential equations (PDEs).
    • Boundary conditions and distributed parameters, such as those in pharmacokinetics, increase the difficulty of solving these PDEs.
    • Existing PDE solutions and their code are frequently problem-specific, limiting broader applicability.

    Purpose of the Study:

    • To develop a generalized computational approach for handling diverse partial differential equations (PDEs) in physiological modeling.
    • To create a method that simplifies the discretization process for complex biological simulations.
    • To enable the automatic generation of simulation code from discretized model equations.

    Main Methods:

    • A replacement scheme is proposed, substituting partial differential terms with numerical solution equations for discretization.
    • Model equations are discretized using this numerical scheme.
    • Dependency analysis is performed on the discretized equations to determine simulation structure.
    • Program code for the simulation is automatically generated based on the dependency analysis.

    Main Results:

    • The replacement scheme effectively handles various types of PDEs encountered in physiological modeling.
    • The method allows for the discretization of complex equations, including those with boundary conditions and distributed parameters.
    • Automated code generation based on dependency analysis streamlines the simulation development process.

    Conclusions:

    • The proposed general approach using a replacement scheme offers a flexible and efficient way to handle PDEs in computational physiological models.
    • This method reduces the need for problem-specific code, promoting reusability and broader application of simulation tools.
    • The automated code generation facilitates faster development and implementation of complex biological simulations.