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We created a new computational framework to accurately model cell dynamics in blood flow. This approach significantly accelerates simulations, offering a three-orders-of-magnitude speedup for biomechanics research.

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Area of Science:

  • Computational Biomechanics
  • Multiscale Modeling
  • Biophysics

Background:

  • Accurate modeling of deformable biological cells in flow is crucial for understanding various physiological and pathological processes.
  • Conventional multiscale modeling simulations are computationally intensive and time-consuming.

Purpose of the Study:

  • To develop a biomechanics-informed online learning framework for simulating cell dynamics.
  • To generalize Jeffery orbits equation for improved accuracy in modeling cell motion.

Main Methods:

  • Utilized supercomputing resources for framework development and validation.
  • Developed a new equation of motion by generalizing Jeffery orbits, incorporating flow conditions and cell deformability.
  • Employed particle-based simulations and learned parameters for validating the new equation.

Main Results:

  • Validated the generalized equation using human platelet motion in shear blood flow.
  • Demonstrated acceleration of conventional multiscale modeling by three orders of magnitude.
  • Achieved high accuracy in simulations, comparable to conventional methods.

Conclusions:

  • The developed online learning framework significantly accelerates cell dynamics simulations.
  • The generalized equation of motion provides an accurate and efficient tool for biomechanics research.
  • This framework has the potential to advance our understanding of cell behavior in various flow conditions.