Computationally Modelling Cholesterol Metabolism and Atherosclerosis

Area of Science:

• Physiology
• Computational Biology
• Pathology

Background:

• Cardiovascular disease (CVD) is the leading global cause of mortality, driven by atherosclerosis.
• Elevated low-density lipoprotein cholesterol (LDL-C) is the primary risk factor for atherosclerosis.
• Existing mathematical models explore cholesterol metabolism and atherosclerosis dynamics separately due to scale differences.

Purpose of the Study:

• To develop a novel integrated mathematical model of whole-body cholesterol metabolism and atherosclerotic plaque formation.
• To bridge the gap between macroscale physiological processes and microscale pathological mechanisms.
• To utilize the integrated model for identifying therapeutic interventions.

Main Methods:

• Combined a mathematical model of cholesterol metabolism with a model of atherosclerotic plaque formation.
• Validated the integrated model's ability to reproduce outputs from parent models.
• Employed the new model to simulate and identify interventions.

Main Results:

• The integrated model successfully reproduces the behavior of its constituent models.
• Demonstrated the model's capability to simulate the interplay between cholesterol metabolism and atherosclerosis.
• Identified potential interventions targeting LDL-C reduction and plaque inhibition.

Conclusions:

• The integrated model provides a unified framework for studying cholesterol metabolism and atherosclerosis.
• This approach facilitates the discovery of novel therapeutic strategies for CVD.
• The model serves as a valuable tool for understanding and mitigating atherosclerosis progression.