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DTSTART:19700308T020000
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DTSTAMP:20210402T160552Z
LOCATION:Track 3
DTSTART;TZID=America/New_York:20201113T124500
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UID:submissions.supercomputing.org_SC20_sess222_ws_cafcw122@linklings.com
SUMMARY:An Efficient, Data-Driven Approach To Model Specific Cancer Cell L
 ines
DESCRIPTION:Workshop\n\nAn Efficient, Data-Driven Approach To Model Specif
 ic Cancer Cell Lines\n\nBalogh, Gounley, Randles\n\nThe transport of cance
 r 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 wit
 h blood flow models that resolve the deformation dynamics of each comprisi
 ng 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 def
 ormability is known to vary between different types of cancer. Such modeli
 ng however is a significant computational undertaking, and thus an approac
 h is needed that is computationally efficient yet sufficiently complex to 
 capture relevant behavior to distinguish between cell lines.\n\nThrough de
 tailed comparisons with experiments, we elucidate a means of using an effi
 cient in silico approach to model specific cancer cell lines. We consider 
 three different cancer cell models: single-membrane, nucleated cell, and n
 ucleated cell with cytoskeleton components. We show that the single membra
 ne model can reproduce experimental behavior for under limited circumstanc
 es, while the nucleated cell model is sufficient to reproduce behavior ove
 r a range of deformations. The cytoskeleton model, while more complex, pro
 vides the same accuracy here as with the nucleated cell model. We outline 
 a systematic approach to use the nucleated cell model to capture differenc
 es in cell behavior to distinguish between different cancer cell lines. Go
 ing forward this can provide new insights into the hemodynamic  mechanisms
  underlying the spread of cancer, and better understand characteristics un
 ique to different cancer types.\n\nRegistration Category: Workshop Reg Pas
 s
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