The physics of living cancer cells reveal new targets
BCRF researcher Sofia Merajver, MD, PhD, co-director of the Breast Oncology Program at the University of Michigan's Comprehensive Cancer Center, has convinced the journal, Cancer Research, that it should add a new, regular section focused on mathematical modeling. Why? Merajver and her team have developed a sophisticated means of studying cancer that takes into account the physics of a living cell.
"The living cell is really a dynamic process,"says Merajver. "We need to consider the properties of physics to help us understand it. In order to develop a drug directed against a given molecule that has real hope of treating cancer, we need to understand how that molecule is sitting in the cell interacting with other molecules," she says. Traditional cancer biology involves taking a sample of cells and halting them in time so they can be studied. Then the researchers look at that slice of cells to understand what signals and pathways are involved. But that doesnlt capture the full picture, says Merajver, professor of internal medicine at the University of Michigan Medical School.
Merajver and her team have developed a mathematical model to help researchers apply time, space, matter and motion concepts to cancer. The mathematical model is designed to help give researchers a complete picture of how a cell interacts with its surrounding environment. By understanding the full complexity of signaling pathways, researchers can better target treatments and identify the most promising potential new drugs.
Merajver and other researchers have learned from this modeling that a well-known signaling pathway transmits information not just in a forward direction, but also backwards. This discovery of "crosstalk" prompts new considerations for developing drugs to inhibit major growth and metastasis pathways in cancer. The crosstalk was missed by conventional methods. Typically when scientists begin to look at a cell, they must make assumptions to simplify the picture of what is happening. "When you make simplifying assumptions, you always run the risk of eliminating critical aspects of your system, but you have no way of knowing what was discarded," says Merajver. "When you simplify, you don't know exactly what you're throwing away because you never looked at the complex case." Mathematical modeling allows researchers to look at the complex case.
Not only has Cancer Research added a new section on mathematical modeling to its pages, it has invited Merajver to lead the effort as a senior editor with her colleague Trachette Jackson, PhD, professor of mathematics at the University of Michigan.
