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Integrating Computational and Biological Hemodynamic Approaches to Improve Modeling of Atherosclerotic Arteries
Thao Nhu Anne Marie Vuong1, Michael Bartolf-Kopp2, Kristina Andelovic2
1Graduate School of Biomedical Engineering, University of New South Wales, Sydney, 2052, Australia.
Insights
Developing better atherosclerosis models is crucial for understanding cardiovascular disease. This review examines computational and biological models, exploring integrated approaches for improved insights.
Area of Science:
- Cardiovascular Science
- Biomedical Engineering
- Computational Biology
Background:
- Atherosclerosis is a leading cause of cardiovascular disease, with significant health and economic impacts.
- Current understanding of atherosclerosis causes and treatments is limited, necessitating advanced modeling.
- Existing computational and biological models have limitations in fully representing atherosclerosis.
Purpose of the Study:
- To critically evaluate existing computational and biological models of atherosclerosis.
- To focus on the hemodynamics within atherosclerotic coronary arteries.
- To explore integrated modeling strategies and emerging technologies for improved atherosclerosis research.
Main Methods:
- Review and critical analysis of computational fluid dynamics (CFD) models.
- Evaluation of in vitro and in vivo biological models.
- Examination of integrated computational and biological modeling approaches.
Main Results:
- Computational models capture geometry and hemodynamics but lack biological complexity.
- Biological models capture biological aspects but often lack human physiological relevance and hemodynamics.
- Integrated models show promise but require further development.
Conclusions:
- There is a need for sophisticated, integrated models of atherosclerosis.
- Advances in imaging, biofabrication, and machine learning are key to developing more effective models.
- Future research should focus on combining computational and biological approaches for comprehensive atherosclerosis study.
Abstract:
Atherosclerosis is the primary cause of cardiovascular disease, resulting in mortality, elevated healthcare costs, diminished productivity, and reduced quality of life for individuals and their communities. This is exacerbated by the limited understanding of its underlying causes and limitations in current therapeutic interventions, highlighting the need for sophisticated models of atherosclerosis. This review critically evaluates the computational and biological models of atherosclerosis, focusing on the study of hemodynamics in atherosclerotic coronary arteries. Computational models account for the geometrical complexities and hemodynamics of the blood vessels and stenoses, but they fail to capture the complex biological processes involved in atherosclerosis. Different in vitro and in vivo biological models can capture aspects of the biological complexity of healthy and stenosed vessels, but rarely mimic the human anatomy and physiological hemodynamics, and require significantly more time, cost, and resources. Therefore, emerging strategies are examined that integrate computational and biological models, and the potential of advances in imaging, biofabrication, and machine learning is explored in developing more effective models of atherosclerosis.
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