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Decoding thrombosis through code: a review of computational models. | LitMetric

Decoding thrombosis through code: a review of computational models.

J Thromb Haemost

Washington University in St Louis, Department of Pediatrics, Division of Hematology and Oncology St Louis, MO, USA; Washington University in St Louis, Department of Biomedical Engineering St Louis, MO, USA. Electronic address:

Published: January 2024

From the molecular level up to a blood vessel, thrombosis and hemostasis involves many interconnected biochemical and biophysical processes over a wide range of length and time scales. Computational modeling has gained eminence in offering insights into these processes beyond what can be obtained from in vitro or in vivo experiments, or clinical measurements. The multiscale and multiphysics nature of thrombosis has inspired a wide range of modeling approaches that aim to address how a thrombus forms and dismantles. Here, we review recent advances in computational modeling with a focus on platelet-based thrombosis. We attempt to summarize the diverse range of modeling efforts straddling the wide-spectrum of physical phenomena, length scales, and time scales; highlighting key advancements and insights from existing studies. Potential information gleaned from models is discussed, ranging from identification of thrombus-prone regions in patient-specific vasculature to modeling thrombus deformation and embolization in response to fluid forces. Furthermore, we highlight several limitations of current models, future directions in the field, and opportunities for clinical translation, to illustrate the state-of-the-art. There are a plethora of opportunity areas for which models can be expanded, ranging from topics of thromboinflammation to platelet production and clearance. Through successes demonstrated in existing studies described here, as well as continued advancements in computational methodologies and computer processing speeds and memory, in silico investigations in thrombosis are poised to bring about significant knowledge growth in the years to come.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11064820PMC
http://dx.doi.org/10.1016/j.jtha.2023.08.021DOI Listing

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