Adaptive Physics Refinement for Anatomic Adhesive Dynamics Simulations

Explicitly simulating the transport of circulating tumor cells (CTCs) across anatomical scales with submicron precision—necessary for capturing ligand-receptor interactions between CTCs and endothelial walls—remains infeasible even on modern supercomputers. In this work, we extend the hybrid CPU-GPU adaptive physics refinement (APR) method to couple a moving finely resolved region capturing adhesive dynamics between a cancer cell and nearby endothelium to a bulk fluid domain. We present algorithmic advancements that: enable the window to traverse vessel walls, resolve adhesive interactions within the moving window, and accelerate adhesive computations with GPUs. We provide an in-depth analysis of key implementation challenges, including trade-offs in data movement, memory footprint, and algorithmic complexity. Leveraging the advanced APR techniques introduced in this work, we simulate adhesive cancer cell transport within a large microfluidic device at a fraction of the computational cost of fully explicit models. This result highlights our method’s ability to significantly expand the accessible problem sizes for adhesive transport simulations, enabling more complex and computationally demanding studies.

Duke Scholars

Author Amanda Randles Biomedical Engineering