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Updated: Aug 5, 2026
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Measuring Pressure Volume Loops in the Mouse
Published on: May 2, 2016
Developing a Digital Twin of the Cardiopulmonary System in a Mouse: Inferring Hemodynamics from Sparse Measurements
Vitaly O Kheyfets1,2, Kenzo Ichimura3, Paul M Heerdt4
1Pediatric Critical Care Medicine, Developmental Lung Biology and CVP Research Laboratories, School of Medicine, University of Colorado, Aurora, CO, USA. vitaly.kheyfets@cuanschutz.edu.
Annals of Biomedical Engineering
|August 4, 2026
Summary
A new 0D cardiopulmonary model accurately simulates mouse physiology and infers individual heart parameters from pressure and volume data. This computational approach aids in understanding cardiopulmonary disease and testing interventions.
Area of Science:
- Cardiovascular Physiology
- Computational Biology
- Rodent Models
Background:
- Rodent models are crucial for studying cardiopulmonary diseases.
- Comprehensive hemodynamic characterization in rodents is challenging.
- Multimodal measurements are often needed for detailed analysis.
Purpose of the Study:
- To evaluate a 0D cardiopulmonary model for simulating mouse-specific physiology.
- To infer individualized cardiac parameters using computational modeling.
- To assess the model's ability to characterize cardiopulmonary function in mice.
Main Methods:
- Developed a 0D cardiopulmonary model incorporating 13 parameters.
- Fitted the model to right ventricular (RV) pressure and volume data from 28 mice.
- Performed sensitivity and identifiability analyses to refine model parameters.
Main Results:
- The model accurately reproduced RV pressure waveforms and volume data.
- Inferred parameters reflected physiological changes under RV overload conditions.
- Model-inferred RV contractility showed moderate correlation with experimental estimates.
Conclusions:
- Subject-specific computational modeling can infer ventricular function and pulmonary hemodynamics.
- This approach provides access to unmeasurable physiological quantities.
- The methodology supports the development of digital twins for disease tracking and intervention testing.
