Decoding thrombosis through code: a review of computational models.

Area of Science:

• Biophysics
• Computational Biology
• Hematology

Background:

• Thrombosis and hemostasis involve complex biochemical and biophysical processes across multiple scales.
• Traditional experimental and clinical methods have limitations in fully elucidating these intricate mechanisms.
• Computational modeling provides a powerful approach to investigate thrombosis beyond experimental constraints.

Purpose of the Study:

• To review recent advancements in computational modeling of thrombosis, with a specific focus on platelet-based mechanisms.
• To summarize diverse modeling approaches, their insights, and applications in understanding thrombus formation and dissolution.
• To identify limitations, future research directions, and opportunities for clinical translation of in silico thrombosis models.

Main Methods:

• Review of existing literature on computational modeling of thrombosis, encompassing multiscale and multiphysics approaches.
• Focus on models addressing platelet behavior, thrombus dynamics, and interactions with fluid forces.
• Analysis of model-derived insights, including identification of thrombus-prone regions and prediction of thrombus behavior.

Main Results:

• Computational models offer insights into thrombus formation, deformation, and embolization, complementing experimental data.
• Models can identify patient-specific vascular regions susceptible to thrombosis.
• Advances span various physical phenomena, length scales, and time scales relevant to thrombosis.

Conclusions:

• In silico investigations are crucial for advancing knowledge in thrombosis and hemostasis.
• Future models can expand to include thromboinflammation, platelet production, and clearance.
• Continued development in computational methods and processing power will drive significant growth in thrombosis research and clinical applications.