Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Modeling total heart function.

Peter J Hunter1, Andrew J Pullan, Bruce H Smaill

  • 1Bioengineering Institute, University of Auckland, New Zealand. p.hunter@auckland.ac.nz

Annual Review of Biomedical Engineering
|October 7, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Structural determinants of re-entrant drivers in atrial fibrillation: insights from digital twins derived from 3D micrometre-resolution imaging of human heart.

The Journal of physiology·2025
Same author

Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2025
Same author

Energy-based bond graph models of glucose transport with SLC transporters.

Biophysical journal·2024
Same author

Pulmonary Veins Function as Echo Chambers in Persistent Atrial Fibrillation: Circuitous Re-Entry Generates Outgoing Wavefronts.

JACC. Clinical electrophysiology·2024
Same author

Chronic intermittent hypoxia remodels catecholaminergic nerve innervation in mouse atria.

The Journal of physiology·2023
Same author

Beatwise ECG Classification for the Detection of Atrial Fibrillation with Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2023
Same journal

Physics-Informed Machine Learning in Biomedical Science and Engineering.

Annual review of biomedical engineering·2026
Same journal

Advancements and Challenges in Computer-Assisted Medical Interventions for Image-Guided Prostate Cancer Treatments.

Annual review of biomedical engineering·2026
Same journal

Recent Advances in mRNA Therapeutic Cancer Vaccines.

Annual review of biomedical engineering·2026
Same journal

Artificial Intelligence-Based Analysis of Laparoscopic Imaging for Intraoperative Surgical Decision Support.

Annual review of biomedical engineering·2026
Same journal

Viscoelasticity of the Heart: An Overview of Viscoelastic Measurements at Different Scales.

Annual review of biomedical engineering·2026
Same journal

Digital Twins for Biofluids.

Annual review of biomedical engineering·2026
See all related articles

Computational heart models integrate cardiac anatomy, tissue properties, and myocyte function. Further inclusion of metabolic and signaling pathways is crucial for understanding heart diseases.

Area of Science:

  • Cardiovascular Physiology
  • Computational Biology
  • Biophysics

Background:

  • The heart's integrated function arises from complex interactions between electrical and mechanical processes.
  • Understanding these processes requires sophisticated computational approaches that model cardiac components at various scales.

Purpose of the Study:

  • To review existing computational models of cardiac electrical and mechanical function.
  • To identify the current framework and limitations in modeling the intact heart's behavior.

Main Methods:

  • Review of computational models focusing on ventricular anatomy.
  • Analysis of models incorporating myocardial tissue properties, ion channels, and myocyte mechanics.
  • Examination of the computational framework linking cellular and tissue structure to whole-heart function.

Related Experiment Videos

Main Results:

  • Computational models provide a framework for linking cardiac cell and tissue structure/function to the integrated behavior of the heart.
  • Current models successfully integrate anatomical, electrical, and mechanical aspects of cardiac function.
  • Significant gaps remain in incorporating metabolic and signal transduction pathways.

Conclusions:

  • Computational modeling has established a framework for understanding cardiac structure-function relationships.
  • Incorporating additional physiological pathways, such as metabolism and signal transduction, is essential for advancing the understanding of cardiac diseases.