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        Modeling Excitable Tissue

        The EMI Framework

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        Contributor(s)
        Tveito, Aslak (editor)
        Mardal, Kent-Andre (editor)
        Rognes, Marie E. (editor)
        Language
        English
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        Abstract
        This open access volume presents a novel computational framework for understanding how collections of excitable cells work. The key approach in the text is to model excitable tissue by representing the individual cells constituting the tissue. This is in stark contrast to the common approach where homogenization is used to develop models where the cells are not explicitly present. The approach allows for very detailed analysis of small collections of excitable cells, but computational challenges limit the applicability in the presence of large collections of cells.
        URI
        https://library.oapen.org/handle/20.500.12657/42934
        Keywords
        Mathematical and Computational Biology; Applications of Mathematics; Mathematical Modeling and Industrial Mathematics; applied mathematics; scientific computing; computational physiology; finite element methods; cardiac modelling; biomechanics; numerical methods; preconditioning; open access; Maths for scientists; Mathematical modelling; Maths for engineers
        DOI
        10.1007/978-3-030-61157-6
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        2021
        Imprint
        Springer International Publishing
        Series
        Simula SpringerBriefs on Computing; Reports on Computational Physiology, 7
        Classification
        Maths for scientists
        Applied mathematics
        Mathematical modelling
        Pages
        100
        Rights
        http://creativecommons.org/licenses/by/4.0/
        • Imported or submitted locally

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        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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