Mathematical modeling to fight breast cancer
Virginia Tech researchers have developed a model that may eventually help explain and overcome drug resistance in breast cancer treatment.
Because cells are tremendously complicated systems, they are not entirely understood. Researchers from engineering, math, and computer science are closing this understanding gap by creating virtual computer models of human cancer cells.
A complete cell model may take years to develop, so researchers are starting with those cellular functions that are most likely to relate to cancer. For example, they are looking at growth, cell division, and apoptosis (intentional cell death). Once they have modeled these functions, they can integrate them to get a simplified virtual cell.
One Lombardi project uses models to understand the process of how drugs cause cancer cells to die. This will help determine which drug (or combination of drugs) is best to treat a specific case of cancer.
Just getting information for these models is complicated. Every tumor is unique and can change with time. Tumors can even have different features in different areas. Making it even more difficult, tissue from tumors gets used up during laboratory experiments, limiting the tests that can be performed on any single sample. To make data collection easier, researchers use specific cancer cell lines (cancer in a petri dish) that change very slowly.
The data collected in laboratory experiments are needed for both developing the models and for testing their validity. This is true interdisciplinary work. The biological and medical researchers develop experiments in the lab and collect comprehensive measurements. The engineers and mathematical modelers then build models based on that data and on the biological understanding of the system. The biologists and modelers then analyze the model and determine the next set of experiments. The data are used to enhance and tune the model, which again suggest more experiments. It’s a long, iterative process that researchers hope will be the final key to understanding cancer and its survival.