The multiple images at left are models of tumor shapes being studied by mathematics Professor Lisette de Pillis. The more “gluttonous” a tumor type is, the more “branchy” (papillary) the resulting tumor shape, as shown in the upper-right corner.


they interact, to discover the ecosystem networks and to develop and evaluate ways to better treat patients who have this cancer.” Even the drugs, devices and therapies developed to treat


disease rely on mathematical models to help prove their safety, effi cacy and cost-effectiveness. “It’s called pharmacoeconomics,” says Anita (McMorrow)


Brogan ’97, head of Decision Analytic Modeling in the Health Economics group at RTI Health Solutions, an independent research organization whose clients include pharmaceutical and biotech companies. “We use different types of models depending on whether we are looking at disease transmission in a population or disease progression in an individual. Once we’ve modeled the disease, the economics come in as we look at how a new intervention—a drug or a device—might impact the course of the disease, patient outcomes and costs.” Those projections are then compared with the outcomes as-


sociated with existing therapies. “A new drug might cost more up front but still save money because it helps prevent costly events from happening in the future. For example, if you have an expensive drug that cures hepatitis C, it may save money in the long-run because you avoid cases of liver disease and liver transplant,” Brogan says. One of the joys of Brogan’s career was working on models


that showed how darunavir—a new drug for HIV patients— was a cost-effective treatment option in Canada. “Our analysis helped the entire country of Canada to get access to it. The


provincial governments added it to their drug lists, so patients can get it and the cost is covered.” While the advantages of using computational and


mathematical tools may be apparent, their use is not actually widespread. “We need better bridges of communication and interac-


tion between people in the clinics and the mathematicians,” de Pillis says. “When I fi rst went to talk to the [Brain Tumor Ecology Collaborative] members in St. Louis, they assumed a mathematical model was simply a statistical analysis of data. So when I explained what we actually mean when we say ‘mathematical model,’ and presented the range of mathemati- cal models we have developed, the doctors were excited about the idea of modeling a growing tumor as a collection of hetero- geneous organisms living in their own ecological system.” When Haddock spotted fellow researchers spending hours


inputting data and formulas into spreadsheets, he decided to co-author a book, Practical Computing For Biologists, to intro- duce them to their computational alternatives. “People hear ‘computational biology’ and automatically


think of DNA and genes, yet the same skills are just as useful for someone who is an ecologist or physiologist,” he says. “You have large amounts of data to process, and being able to do that repeatedly in a way that you can document for someone else is essential to any biological science now.”


SPRING 2013 Har vey Mudd College 21


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