📢 New paper out !!
This work investigates data assimilation of time-resolved microscopy data to adapt the prediction of the spatiotemporal response of breast cancer cells to chemotherapy with doxorubicin as new data becomes available during the experiments.
This approach can help us understand the complex dynamics underlying tumor growth and response to treatment, such as the development of chemoresistance, which is a central cause of treatment failure in breast cancer patients. By adapting our model parameterization with incoming data, we can anticipate the onset of chemoresistance, which could be leveraged to personalize treatment regimens to improve therapeutic outcomes.
Check out this great work led by Hugo Miniere and colleagues at UT Center for Computational Oncology !!
https://lnkd.in/e6w7ARgF
Consider checking out the new paper by UT Center for Computational Oncology members Hugo Miniere, Ernesto Lima, Guillermo Lorenzo Gómez, David Hormuth, Sophia Ty, Amy Brock, and Tom Yankeelov focused on predicting the spatiotemporal response of breast cancer cells treated with doxorubicin 10.1080/15384047.2024.2321769. Also, happy Leap Day!