Computational cardiac physiology for new modelers: Origins, foundations, and future
Jussi T Koivumäki1, Johan Hoffman2, Mary M Maleckar3
1Faculty of Medicine and Health Technology, and Centre of Excellence in Body-on-Chip Research, Tampere University, Tampere, Finland.
Acta Physiologica (Oxford, England)
|August 12, 2022
Summary
Mathematical models of the cardiovascular system have advanced significantly, aiding physiological research. Overcoming barriers between experimental and computational scientists is crucial for future progress in cardiovascular physiology.
Area of Science:
- Cardiovascular Physiology
- Computational Biology
- Mathematical Modeling
Background:
- Mathematical models of the cardiovascular system have evolved since the 19th century.
- Advancements in experimental techniques, numerical methods, and computing power have enabled detailed, multi-scale models.
- These models condense physiological knowledge, aiding hypothesis testing and research interpretation.
Purpose of the Study:
- To provide a historical overview of mathematical and computational models in cardiovascular physiology.
- To discuss the current state-of-the-art in cardiovascular modeling.
- To advocate for closer integration between experimental and computational research in physiology.
Main Methods:
- Review of historical development of mathematical and computational models in cardiovascular physiology.
- Analysis of current state-of-the-art models.
- Discussion of barriers and challenges to interdisciplinary integration.
Main Results:
- Mathematical and computational models have substantially improved understanding of cardiovascular physiology.
- Significant barriers persist between experimental and computational research communities.
- A tighter integration is needed to fully leverage the synergy between these fields.
Conclusions:
- Mathematical and computational models are invaluable tools in cardiovascular physiology.
- Bridging the gap between experimental and computational approaches is essential for future advancements.
- Addressing identified obstacles will unlock greater potential in physiological research.
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