Speakers
Description
Mathematical modeling has proven to be useful for studying many biological fields and the increased focus on translational models has been a major boon to clinical research. Models benefit each stage of clinical research and have resulted in significant improvements to disease monitoring, treatment development, and the scheduling of treatment regimes \cite{scibilia2025mathematical}. Clinically focused models have identified novel cell-cell interactions and biomarkers that inform clinicians of disease development and progression. Further exploration of cellular interactions and biomarkers through modeling allows expedited investigation of new therapeutic options. Models allow newly developed therapies to be evaluated through virtual clinical trials such as with digital twins representing individual patients or synthetic patient cohorts. Additional testing of treatment scheduling with modeling has revealed new insights allowing clinicians to reduce strain on patients while more effectively targeting treatment resistance and improving patient outcome. In this minisymposium we will highlight research that bridges traditional mathematical modeling with translational research. In particular, we will showcase math models that are developed with clinical data or in collaboration with clinicians for clinically applicable models with real world utility.
Bibliography
@article{scibilia2025mathematical,
title={Mathematical Oncology: How Modeling Is Transforming Clinical Decision-Making},
author={Scibilia, Kevin R and Gallagher, Kit and Masud, MA and Robertson-Tessi, Mark and Gatenbee, Chandler D and West, Jeffrey and Llamas, Paul and Prabhakaran, Sandhya and Gallaher, Jill and Anderson, Alexander RA},
journal={Cancer research},
volume={85},
number={24},
pages={4866--4879},
year={2025},
publisher={American Association for Cancer Research}
}
