Mathematical Modeling of Atherosclerotic Disease Mechanisms

Mathematical models of atherosclerosis integrate biophysical, biochemical and cellular processes to elucidate plaque initiation, progression and potential regression. By representing the transport and modification of lipoproteins, recruitment and phenotypic modulation of monocytes and macrophages, extracellular matrix dynamics and smooth muscle cell proliferation, such models recapitulate the complex interplay that drives arterial lesion formation. Techniques range from systems of partial differential equations capturing spatial–temporal reaction–diffusion of key species to agent-based simulations of individual cell behaviours under haemodynamic stimuli. These in silico frameworks serve as virtual laboratories for hypothesis testing, sensitivity analysis and patient-specific prediction, offering insights into the roles of lipid flux, endothelial function, inflammatory mediators and mechanical forces. Collectively, they aim to improve risk stratification, optimise therapeutic targets and guide personalised strategies to prevent plaque destabilisation and subsequent cardiovascular events.

Research from Nature Portfolio

A three-dimensional computational biomechanics model has been devised to simulate atherosclerotic plaque growth in patient-specific coronary arteries reconstructed from serial CT coronary angiography. This framework couples endothelial shear stress with the accumulation of low- and high-density lipoproteins, monocyte infiltration, macrophage and foam-cell dynamics, cytokine signalling and smooth muscle cell proliferation. Validation against longitudinal imaging demonstrates high correlation between simulated and observed lumen and plaque areas, achieving predictive accuracy of lesion progression. The study highlights independent computational predictors of disease advancement and underlines the potential of personalised in silico models to identify regions at risk and inform clinical decision-making.

Mathematical Modeling of Atherosclerotic Disease Mechanisms publication trend

The graph below shows the total number of articles in mathematical modeling of atherosclerotic disease mechanisms across all publications each year (not limited to Nature Index journals).

Technical terms

Low-density lipoprotein (LDL): Cholesterol-rich particles that penetrate the endothelium and undergo oxidation, initiating atherogenesis.

Foam cell: Lipid-laden macrophage that accumulates in the intimal layer, contributing to plaque growth.

Partial differential equation (PDE): Mathematical equation modelling how quantities change over space and time, used for reaction–diffusion systems.

Agent-based model (ABM): Computational framework in which individual cells or particles follow rules to simulate collective phenomena.

Wall shear stress (WSS): Tangential force of flowing blood on endothelial surfaces, influencing cell adhesion and gene expression.

Bifurcation: A qualitative change in system behaviour as parameters cross critical thresholds, often studied in dynamical systems.

References

1. Computational modelling of atherosclerosis. Briefings in Bioinformatics (2015).
2. Simulation of atherosclerotic plaque growth using computational biomechanics and patient-specific data. Scientific Reports (2020).
3. Mathematical modeling of inflammatory processes of atherosclerosis. Mathematical Modelling of Natural Phenomena (2022).
4. An agent-based model of leukocyte transendothelial migration during atherogenesis. PLOS Computational Biology (2017).
5. Modeling fibrous cap formation in atherosclerotic plaque development: stability and oscillatory behavior. Advances in Continuous and Discrete Models (2017).