Biventricular Interaction During Acute Left Ventricular Ischemia in Mice: A Combined In-Vivo and In-Silico Approach

Area of Science:

• Cardiovascular Physiology
• Computational Biology
• Biomedical Engineering

Background:

• Computational models are valuable for studying cardiovascular dynamics but face challenges in parameterization and data integration.
• Advances in data collection and analysis offer opportunities to improve model accuracy and applicability.
• Multiscale modeling is crucial for capturing complex interactions across different physiological scales.

Purpose of the Study:

• To develop and validate a multiscale, biventricular interaction model for simulating left ventricular (LV) ischemia in mice.
• To identify key model parameters influencing cardiovascular predictions through sensitivity analysis.
• To quantitatively reproduce in-vivo experimental data and qualitatively match prior findings on myocardial infarction.

Main Methods:

• Combined a multiscale, biventricular interaction model with mouse data collected before and after induced LV ischemia.
• Performed sensitivity analyses to pinpoint influential parameters affecting pressure and volume predictions.
• Calibrated influential model parameters using baseline biventricular pressure-volume loop data.
• Simulated acute LV ischemia using the calibrated model and compared outputs with baseline and experimental data.

Main Results:

• Baseline model simulations demonstrated strong agreement with experimental LV and RV pressure-volume loop data.
• Simulated ischemia predictions aligned with recorded RV data and existing knowledge of LV function post-myocardial infarction.
• The model successfully reproduced in-vivo data, highlighting the importance of biventricular interaction and systems-level dynamics.

Conclusions:

• A multiscale computational model incorporating biventricular mechanical interaction and systems-level cardiovascular dynamics can accurately represent in-vivo data.
• This modeling approach provides a powerful tool for investigating cardiovascular pathologies like myocardial infarction.
• The study validates the utility of computational modeling in bridging the gap between experimental data and complex physiological processes.