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Capturing Multiscale Dynamics of Aortic Valve Calcification with a Coupled Fluid-Structure and Systems Biology Model.

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This study introduces a computational model linking blood flow and cellular signaling to predict calcific aortic valve disease progression. The model shows how tissue stiffening accelerates calcification by altering mechanical forces and biochemical pathways.

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Area of Science:

  • Multiphysics computational modeling
  • Cardiovascular disease research
  • Biomedical engineering

Background:

  • Calcific aortic valve disease (CAVD) involves complex interactions between hemodynamics, tissue mechanics, and cellular signaling.
  • Existing models often focus on fluid-structure interaction (FSI) or systems biology (SB) separately, failing to capture disease feedback loops.
  • Multiscale coupling of these phenomena is crucial for understanding and predicting CAVD progression.

Purpose of the Study:

  • To develop and present a proof-of-principle multiphysics computational framework.
  • To couple 3D FSI simulations of aortic valve dynamics with a mechanistic SB model of calcification signaling.
  • To investigate the impact of mechanical changes on biochemical pathways driving CAVD.

Main Methods:

  • Developed a computational framework integrating 3D FSI simulations with a mechanistic SB model.
  • FSI module simulated pulsatile blood flow and leaflet deformation, calculating wall shear stresses and tissue strains.
  • SB module modeled key biochemical pathways (inflammation, TGF-β/SMAD, NO inhibition) using mechanical outputs as inputs.

Main Results:

  • Simulations predicted long-term calcification trajectories based on valve thickness.
  • Fibrosis-induced stiffening was shown to lower shear stress and nitric oxide (NO) synthesis.
  • Reduced NO and enhanced TGF-β activation were linked to accelerated calcification.

Conclusions:

  • The coupled framework demonstrates the utility of integrating physics-based hemodynamics with systems-level biochemistry.
  • This multiscale modeling platform can advance the study of cardiovascular diseases like CAVD.
  • Future work will focus on two-way coupling and incorporating additional biological pathways for enhanced predictive capability.