# Technology for efficient simulation of cancer cell transport

> **NIH NIH U01** · DUKE UNIVERSITY · 2021 · $376,073

## Abstract

Cancer is the attributed cause of death in one in four cases in the United States and metastasis,
a complex multistep process leading to the spread of tumors, is responsible for more than 90%
of these deaths. However, predicting the location of these secondary tumor sites is still an
elusive goal. One of the fundamental hurdles is to understand the trajectory of cell movement
through the vascular system and the likelihood of penetration of the vessel wall. Studies have
demonstrated that more than 50% of cancer metastatic sites could be explained by the blood
flow pattern between the primary and secondary; however, the development of predictive
models is still needed. Insight into the underlying mechanisms of cancer metastasis will provide
insight into disease progression and lead to the development of new diagnostic or therapeutic
methods targeting regions of the vasculature likely to incur secondary tumor sites.
Tools that can be easily tuned to allow not only patient-specific but cell-specific modeling would
complement ongoing in vitro experiments and provide this critical insight. Such computational
models would allow researchers to probe the influence of different biophysical properties on
cancer-specific cell behavior without the need for either expensive experimental trials for each
cell-type or extrapolate from findings for one cancer to apply to another. An expected outcome
of this research to create a usable, scalable, and extensible software framework for use by the
wider biomedical research community to study the role of biophysical properties on a cell's
transport and potential arrest. On such a platform, users will be able to introduce models of
their cells-of-interest and perform simulations on them with models we (or others in the
community) have developed. The ability to seamlessly introduce new cell-types with minimal
effort will foster entirely new collaborations between researchers and provide biologists who
would not traditionally leverage computational resources to study cell-specific properties in the
context of realistic vascular geometries. This work will set the stage for future studies expanding
the capabilities of this open source model.

## Key facts

- **NIH application ID:** 10239243
- **Project number:** 5U01CA253511-02
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Amanda E Randles
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $376,073
- **Award type:** 5
- **Project period:** 2020-08-15 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10239243

## Citation

> US National Institutes of Health, RePORTER application 10239243, Technology for efficient simulation of cancer cell transport (5U01CA253511-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10239243. Licensed CC0.

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