lifex-ep: a robust and efficient software for cardiac electrophysiology simulations
Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and...
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| Published in: | BMC bioinformatics Vol. 24; no. 1; pp. 1 - 38 |
|---|---|
| Main Authors: | , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
13.10.2023
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online Access: | Get full text |
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| Abstract | Background
Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.
Results
This work introduces
life
x
-ep
, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions.
life
x
-ep
employs the monodomain equation to model the heart’s electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally,
life
x
-ep
integrates the generation of myocardial fibers based on Laplace–Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within
life
x
-fiber
. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying
life
x
-ep
, along with comprehensive implementation details and instructions for users.
life
x
-ep
features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of
life
x
-ep
through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.
Conclusions
life
x
-ep
provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface.
life
x
-ep
represents a valuable tool for conducting in silico patient-specific simulations. |
|---|---|
| AbstractList | Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. This work introduces [formula omitted]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [formula omitted]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [formula omitted]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [formula omitted]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [formula omitted]-ep, along with comprehensive implementation details and instructions for users. [formula omitted]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [formula omitted]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. [formula omitted]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [formula omitted]-ep represents a valuable tool for conducting in silico patient-specific simulations. Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. Results This work introduces [formula omitted]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [formula omitted]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [formula omitted]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [formula omitted]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [formula omitted]-ep, along with comprehensive implementation details and instructions for users. [formula omitted]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [formula omitted]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. Conclusions [formula omitted]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [formula omitted]-ep represents a valuable tool for conducting in silico patient-specific simulations. Keywords: Cardiac electrophysiology, Computational cardiology, High-performance computing, Mathematical modeling, Finite element method BackgroundSimulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.ResultsThis work introduces \(\texttt {life}^{\text{x}}\)-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. \(\texttt {life}^{\text{x}}\)-ep employs the monodomain equation to model the heart’s electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, \(\texttt {life}^{\text{x}}\)-ep integrates the generation of myocardial fibers based on Laplace–Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within \(\texttt {life}^{\text{x}}\)-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying \(\texttt {life}^{\text{x}}\)-ep, along with comprehensive implementation details and instructions for users. \(\texttt {life}^{\text{x}}\)-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of \(\texttt {life}^{\text{x}}\)-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.Conclusions\(\texttt {life}^{\text{x}}\)-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. \(\texttt {life}^{\text{x}}\)-ep represents a valuable tool for conducting in silico patient-specific simulations. Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. Results This work introduces life x -ep , a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. life x -ep employs the monodomain equation to model the heart’s electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, life x -ep integrates the generation of myocardial fibers based on Laplace–Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within life x -fiber . As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying life x -ep , along with comprehensive implementation details and instructions for users. life x -ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of life x -ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. Conclusions life x -ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. life x -ep represents a valuable tool for conducting in silico patient-specific simulations. Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.BACKGROUNDSimulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community.This work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.RESULTSThis work introduces [Formula: see text]-ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. [Formula: see text]-ep employs the monodomain equation to model the heart's electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, [Formula: see text]-ep integrates the generation of myocardial fibers based on Laplace-Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within [Formula: see text]-fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying [Formula: see text]-ep, along with comprehensive implementation details and instructions for users. [Formula: see text]-ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of [Formula: see text]-ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files.[Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations.CONCLUSIONS[Formula: see text]-ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. [Formula: see text]-ep represents a valuable tool for conducting in silico patient-specific simulations. Abstract Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for streamlined, accurate, and high-performance computational tools. Despite the dedicated endeavors of various research teams, comprehensive and user-friendly software programs for cardiac simulations, capable of accurately replicating both normal and pathological conditions, are still in the process of achieving full maturity within the scientific community. Results This work introduces $$\texttt {life}^{\text{x}}$$ life x -ep, a publicly available software for numerical simulations of the electrophysiology activity of the cardiac muscle, under both normal and pathological conditions. $$\texttt {life}^{\text{x}}$$ life x -ep employs the monodomain equation to model the heart’s electrical activity. It incorporates both phenomenological and second-generation ionic models. These models are discretized using the Finite Element method on tetrahedral or hexahedral meshes. Additionally, $$\texttt {life}^{\text{x}}$$ life x -ep integrates the generation of myocardial fibers based on Laplace–Dirichlet Rule-Based Methods, previously released in Africa et al., 2023, within $$\texttt {life}^{\text{x}}$$ life x -fiber. As an alternative, users can also choose to import myofibers from a file. This paper provides a concise overview of the mathematical models and numerical methods underlying $$\texttt {life}^{\text{x}}$$ life x -ep, along with comprehensive implementation details and instructions for users. $$\texttt {life}^{\text{x}}$$ life x -ep features exceptional parallel speedup, scaling efficiently when using up to thousands of cores, and its implementation has been verified against an established benchmark problem for computational electrophysiology. We showcase the key features of $$\texttt {life}^{\text{x}}$$ life x -ep through various idealized and realistic simulations conducted in both normal and pathological scenarios. Furthermore, the software offers a user-friendly and flexible interface, simplifying the setup of simulations using self-documenting parameter files. Conclusions $$\texttt {life}^{\text{x}}$$ life x -ep provides easy access to cardiac electrophysiology simulations for a wide user community. It offers a computational tool that integrates models and accurate methods for simulating cardiac electrophysiology within a high-performance framework, while maintaining a user-friendly interface. $$\texttt {life}^{\text{x}}$$ life x -ep represents a valuable tool for conducting in silico patient-specific simulations. |
| ArticleNumber | 389 |
| Audience | Academic |
| Author | Pagani, Stefano Quarteroni, Alfio Africa, Pasquale Claudio Regazzoni, Francesco Salvador, Matteo Dede’, Luca Bucelli, Michele Fedele, Marco Piersanti, Roberto |
| Author_xml | – sequence: 1 givenname: Pasquale Claudio surname: Africa fullname: Africa, Pasquale Claudio organization: MOX, Department of Mathematics, Politecnico di Milano, mathLab, Mathematics Area, SISSA International School for Advanced Studies – sequence: 2 givenname: Roberto surname: Piersanti fullname: Piersanti, Roberto email: roberto.piersanti@polimi.it organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 3 givenname: Francesco surname: Regazzoni fullname: Regazzoni, Francesco organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 4 givenname: Michele surname: Bucelli fullname: Bucelli, Michele organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 5 givenname: Matteo surname: Salvador fullname: Salvador, Matteo organization: MOX, Department of Mathematics, Politecnico di Milano, Institute for Computational and Mathematical Engineering, Stanford University – sequence: 6 givenname: Marco surname: Fedele fullname: Fedele, Marco organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 7 givenname: Stefano surname: Pagani fullname: Pagani, Stefano organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 8 givenname: Luca surname: Dede’ fullname: Dede’, Luca organization: MOX, Department of Mathematics, Politecnico di Milano – sequence: 9 givenname: Alfio surname: Quarteroni fullname: Quarteroni, Alfio organization: MOX, Department of Mathematics, Politecnico di Milano, Institute of Mathematics, École Polytechnique Fédérale de Lausanne |
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| CitedBy_id | crossref_primary_10_1371_journal_pone_0303674 crossref_primary_10_1016_j_camwa_2024_04_014 crossref_primary_10_1145_3748817 crossref_primary_10_1016_j_compbiomed_2025_109774 crossref_primary_10_1016_j_cma_2025_118001 crossref_primary_10_1098_rsta_2023_0384 crossref_primary_10_1137_24M1643888 crossref_primary_10_1016_j_cma_2024_117077 crossref_primary_10_1016_j_compbiomed_2024_109529 crossref_primary_10_1007_s10237_024_01878_8 crossref_primary_10_1142_S0218202525500125 crossref_primary_10_1145_3716310 crossref_primary_10_1016_j_compbiomed_2025_110975 crossref_primary_10_1016_j_jcp_2024_112885 |
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| Keywords | Finite element method Primary 92-04 secondary 35-04 High-performance computing Mathematical modeling 68N30 65M60 65Y05 Cardiac electrophysiology Computational cardiology 92C50 |
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| License | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
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Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for... Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for... Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for... BackgroundSimulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the need for... Abstract Background Simulating the cardiac function requires the numerical solution of multi-physics and multi-scale mathematical models. This underscores the... |
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| SubjectTerms | Algorithms Arrhythmia Bioinformatics Biomedical and Life Sciences Cardiac arrhythmia Cardiac electrophysiology Cardiac function Cardiac muscle Cardiology Cardiomyocytes Care and treatment Computational Biology/Bioinformatics Computational cardiology Computer Appl. in Life Sciences Computer applications Computer simulation Diagnosis Dirichlet problem Electrocardiography Electrophysiology Fibers Finite element method Heart High-performance computing Life Sciences Mathematical modeling Mathematical models Microarrays Numerical analysis Numerical methods Ordinary differential equations Partial differential equations Propagation Robustness (mathematics) Simulation Simulation methods Software |
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| Title | lifex-ep: a robust and efficient software for cardiac electrophysiology simulations |
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