Computational modelling of atherosclerosis

Area of Science:

• Cardiovascular Disease Research
• Computational Biology
• Mathematical Modeling

Background:

• Atherosclerosis is a major cardiovascular disease pathology linked to elevated cholesterol.
• An estimated 2.5 million deaths annually are attributed to high cholesterol, posing a global health risk.
• The complex interplay of hemodynamics, endothelial damage, immunity, and sterol biochemistry drives atherosclerosis, yet remains poorly understood.

Purpose of the Study:

• To review the current state of mathematical and computational modeling in atherosclerosis research.
• To consolidate recent findings and highlight the scope of existing work.
• To identify knowledge gaps and future research opportunities in modeling atherosclerosis.

Main Methods:

• Utilizing mathematical and computational modeling as a framework to study complex, multi-scale disease dynamics.
• Integrating various etiological factors within a unified computational model.
• Reviewing and synthesizing existing literature on atherosclerosis modeling.

Main Results:

• A growing interest in applying computational models to understand atherosclerosis is evident.
• Existing models provide an integrated description of the disease, serving as in silico experimental systems.
• The current body of work, though limited, shows promise for developing therapeutic hypotheses.

Conclusions:

• Mathematical and computational modeling is a crucial framework for understanding the dynamics of atherosclerosis.
• Further research is needed to address the gaps identified in current modeling approaches.
• This nascent field holds significant potential for advancing our understanding and treatment of cardiovascular disease.