UT turns supercomputers toward cancer research

The computer that took the Apollo 11 astronauts to the moon in 1969 was a marvel of technology. Today, the phones in our pockets have more than 100,000 times the power of the Apollo 11 computer. Our laptops and desktop computers are even more powerful. If we take hundreds of our most powerful computers and link them together, we get a supercomputer. The same analytical tasks that would take a research scientist thousands of years to complete manually can be accomplished in seconds with supercomputers. I use these powerful computers to process enormous amounts of data from all over the world. This allows me to investigate the biology of cancer cells and new anti-cancer drugs.

I combine traditional laboratory research methods that use Petri dishes, test tubes, and microscopes with computational biology techniques that use mathematical modeling and simulations to answer important research questions. How do cancer cells resist chemotherapy? How do cancer cells metastasize? My answers to these questions are contributing to the development of better anti-cancer drugs. My recent work focuses on a specific type of protein in the human body called kinases. Kinases turn other proteins on and off.

Why am I interested in kinases? Humans have more than 500 different kinases that control complicated molecular pathways within our cells. Imagine a kinase as a hand, just floating around, looking for a specific partner protein. Now, imagine the specific partner protein as a light-switch, just waiting to be turned on or off. When a kinase (hand) meets a protein (light-switch), it flicks the switch. If the kinase turns the protein off, then the protein will stop working and peacefully drift around until another kinase comes by and flicks it back on. When a kinase turns a protein on, then that protein will start working as an enzyme, messenger, or transporter. This is how kinases regulate the behavior of our cells. I want to better understand kinases because kinases interact with other proteins to control the molecular pathways ultimately responsible for important cellular actions, like cell growth and cell death.

As I identify which kinases turn on the proteins that tell a cancer cell to grow, we can design drugs that inhibit the harmful pro-cancer effects of those kinases. As I identify which kinases turn on the proteins that tell a cancer cell to die, we can design drugs that enhance the anti-cancer effects of those kinases.

Using traditional laboratory techniques to precisely identify kinase activity is difficult, time-consuming, and expensive. Part of the reason for this is that traditional laboratory techniques examine only one kinase at a time. When you combine traditional laboratory techniques with advanced computational biology techniques, you can significantly improve the quality and quantity of your research and produce experimental results that give you much more useful information.

How does this work? First, imagine a big biomedical research laboratory: long steel benches full of glass beakers, plastic tubing, shakers, mixers, and vials full of chemicals. Take all that equipment and shrink it down.

The long steel bench is now the size of a credit card. The big glass beakers have become tiny porcelain wells. Small tubes, as thin as a strand of hair, connect everything. And a powerful computer controls it all.

You line each tiny porcelain well with a different protein, and then pump a solution of many different kinases back and forth through the wells, like mouthwash being swished through teeth. As they swish, the kinases touch everything, bumping into all the different types of proteins lining the wells. If a kinase meets one of its partner proteins, it flips the switch on that protein and that protein literally lights up. All the while, a camera is taking photos to measure how much light is created. Because each well is lined by only one type of protein and because each well is located at a specific physical location, these photos can determine which proteins are being switched on and off by the kinases.

Custom-made computer programs can then identify when the activity of each kinase changes. By using these principles to design more complex, more powerful cancer biology experiments, I identify new kinase targets for the treatment of pancreatic cancer. Clinical outcomes for pancreatic cancer patients remain dismal, with decades of research unable to bring survival rates above 9 percent. The end goal of my research is to use my knowledge of kinases to develop drugs that increase survival for pancreatic cancer patients.

Justin Creeden is earning his MD/​PhD in the University of Toledo College of Medicine and Life Sciences Biomedical Science and MD Programs. Justin is doing his doctoral research in the laboratory of Robert Mccullumsmith,MD/ PhD, in the Department of Neuroscience. For more information, contact Justin.Creeden@rockets.utoledo.edu or go to utoledo.edu/​med/​grad/​biomedical