Bridging Mathematical Modeling and Experimental Data to Optimize Cancer Therapy

Abstract: Optimizing cancer treatment will increasingly rely on close coupling between experimental research and mathematical modeling. A central obstacle in oncology is our incomplete understanding of how therapies reshape the tumor microenvironment and how immune activity drives tumor regression. This gap is especially acute in aggressive cancers such as triple-negative breast cancer (TNBC), which lacks standard biomarkers and has limited treatment options. Our collaborative work develops a quantitative framework for studying combined immune and chemotherapeutic interventions. Using a novel radiolabeled T-cell tracking approach, we follow immunomodulation over time and use these measurements to guide the design of optimal therapy schedules. In this talk, I will present ordinary differential equation (ODE) models of combination therapy dynamics and compare model performance with and without T-cell data. I will also discuss broader directions for this work, including extensions to organ-level analysis and the use of optimal control methods to refine treatment strategies. By integrating experiment and theory, we aim to strengthen the predictive power of cancer treatment models and move toward more effective, personalized therapeutic protocols.

Biologyprobabilitystatistics theorydata analysis, statistics and probability

Audience: researchers in the topic

( chat )

Comments: http://www.math.fsu.edu/~cogan

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Asymptotic Methods in Statistics

Series comments: One can find video files and slides of talks at the seminar on Asymptotic Methods in Statistics here: www.youtube.com/@SeminarforAsymptoticMethods

A link to a slide is below the description of the video, namely shorturl.at/jUAhL and shorturl.at/6wjUA shorturl.at/rdpqs

Alas the links do not work in one click: you should emphasize them with a mouse and select, e.g., "Go to shorturl.at/rdpqs".

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