Guidelines for mechanistic modeling and analysis in cardiovascular research

Area of Science:

• Cardiovascular Research
• Computational Biology
• Biomedical Engineering

Background:

• Computational (in silico) models offer a noninvasive method for studying cardiovascular function.
• These models aid in analyzing experimental and clinical data to understand cardiovascular physiology.
• Advancements in computing have enabled complex, multi-scale cardiovascular simulations.

Purpose of the Study:

• To establish consensus-based best practices for in silico computational modeling in cardiovascular research.
• To address the need for standardized guidelines in implementing computational models.
• To promote reproducible research in the field of cardiovascular science.

Main Methods:

• Development of mechanistic computational models.
• Rigorous analysis and formal calibration of models using statistical principles.
• Integration of computational modeling with experimental and clinical data.

Main Results:

• A comprehensive set of guidelines for best practices in cardiovascular computational modeling.
• Methods for mechanistic model development, analysis, and calibration.
• Demonstration of the synergistic value of in silico models with empirical data.

Conclusions:

• Implementing these best practices enhances the credibility and reproducibility of cardiovascular research.
• Standardized guidelines are crucial for the effective use of computational models.
• In silico modeling, when rigorously applied, significantly complements experimental and clinical cardiovascular studies.