SC20 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

An Efficient, Data-Driven Approach To Model Specific Cancer Cell Lines

Workshop:CAFCW20: Sixth Computational Approaches for Cancer Workshop

Authors: Peter Balogh (Duke University), John Gounley (Oak Ridge National Laboratory), and Amanda Randles (Duke University)

Abstract: The transport of cancer cells through the microcirculation is a fundamental component underlying the progression and spread of cancer. Simulations offer the potential to provide new insights owing to the level of detail that can be captured with blood flow models that resolve the deformation dynamics of each comprising cell. In this context, it is important to have a cancer cell model that can accurately represent a specific cell line, given that cancer cell deformability is known to vary between different types of cancer. Such modeling however is a significant computational undertaking, and thus an approach is needed that is computationally efficient yet sufficiently complex to capture relevant behavior to distinguish between cell lines.

Through detailed comparisons with experiments, we elucidate a means of using an efficient in silico approach to model specific cancer cell lines. We consider three different cancer cell models: single-membrane, nucleated cell, and nucleated cell with cytoskeleton components. We show that the single membrane model can reproduce experimental behavior for under limited circumstances, while the nucleated cell model is sufficient to reproduce behavior over a range of deformations. The cytoskeleton model, while more complex, provides the same accuracy here as with the nucleated cell model. We outline a systematic approach to use the nucleated cell model to capture differences in cell behavior to distinguish between different cancer cell lines. Going forward this can provide new insights into the hemodynamic mechanisms underlying the spread of cancer, and better understand characteristics unique to different cancer types.


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