ECE: Electrical & Computer Engineering
ECE News

Mathematical modeling to fight breast cancer

breast cancer cell

Recently, experimentalists at Georgetown found that a key chaperone protein known as BIP or GRP78, which helps fold newly synthesized proteins, was over-expressed in a resistant breast cancer cell line compared to a sensitive cancer cell line. Click image to expand

Virginia Tech researchers have developed a model that may eventually help explain and overcome drug resistance in breast cancer treatment.

As part of the Georgetown Center for Cancer Systems Biology, ECE faculty members William Baumann, Joseph Wang, and Jason Xuan, with John Tyson of Biology, have been using mathematical modeling and machine learning approaches to understand the causes of drug resistance in breast cancer treatment.

Recently, experimentalists at Georgetown found that a key chaperone protein known as BIP or GRP78, which helps fold newly synthesized proteins, was over-expressed in a resistant breast cancer cell line compared to a sensitive cancer cell line. Reducing the expression of BIP in the resistant cell line caused the cells to become sensitive to drugs, while enhancing its expression in the sensitive cell line caused the cells to become resistant to drugs.

Understanding this intriguing experimental result is complicated by the interaction of three complex cellular subsystems: the Unfolded Protein Response, autophagy (self-eating), and apoptosis (programmed death).

Virginia Tech researchers mathematically modeled each of the three subsystems using nonlinear ordinary differential equations to capture their basic behavior, then combined the subsystems as depicted in the figure above. While much is unknown about some of the interactions, the model was able to recapitulate the experimental results. The model is being used to suggest new experiments to improve the model and understanding of drug resistance. Ultimately, the model can be used to find combinations of drugs to kill the resistant cancer cells, according to Baumann.