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Feilim MacGabhann on Mathematical Models as Versatile Tools


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Feilim MacGabhann on Mathematical Models as Versatile Tools

By Melissa Hendricks

Feilim MacGabhann on Mathematical Models as Versatile Tools

Feilim Mac Gabhann; credit: David Hopkins

Feilim Mac Gabhann studies computational medicine in the biomedical engineering department. He develops and uses mathematical models as a “sandbox” for testing ideas about how key biological processes work. 

Your research takes places at a crossroads of disciplines. Did you begin your research in biology, engineering, computing or something else?

MAC GABHANN: From a young age I’ve been tinkering with programming and simulations—basically how you can create a computational model of a physical process. I continued this interest at University College Dublin in Ireland, where I studied chemical engineering. As a chemical engineer, I studied transport and kinetics—how molecules move around and interact with each other. We can create and use mathematical models to study these processes.

After college I worked for a couple years as a management consultant, and then I came to Johns Hopkins to do a Ph.D. in biomedical engineering. That’s when I started applying theories of chemical engineering—those models of transport and kinetics—to problems of biomedical engineering.

What do you enjoy about developing and using mathematical models?

MAC GABHANN: Models can be like virtual tinker toys. They provide us with a sandbox in which to test our ideas about how key biological processes work. We create a working model of the system we are exploring, and then we test it and try to break it. By simulating the system as a whole, rather than isolated pieces, we can predict better therapies. Modeling leads to ideas and insights that we would never see just through thinking or through reductionist experiments. Also, there are often cost or practical limitations to the experiments we can perform, but not so with models; thus they make the scientific process much more efficient by helping us to zero in on the key experiments to try.

Can we return to your stint as a management consultant? I’m guessing that few scientists have that title on their résumés. What made you choose that job?

MAC GABHANN: When I finished my undergraduate degree, I wanted to take some time to decide what direction to go for graduate school. It was a great opportunity to try something different, and to work in a non-academic setting. But in fact, the work I did in management consulting helped prepare me for the research I currently do. One of my specialties during that period was modeling—financial modeling.

I chose consulting because few other jobs can give you as broad an exposure to multiple projects, or as fast a learning curve. And it was a great experience. I think that the most important translatable skill I learned there was the ability to communicate clearly and to many different audiences. This is crucial in modern science.

Would you recommend that a student who is interested in computational medicine follow your career path? What’s the best way for someone interested in this specialized area of engineering and medicine to prepare?

MAC GABHANN: Computational medicine is going to continue to grow and will be key to the new era of medicine. Along with academic institutions, most pharmaceutical companies have established computational research programs in order to improve all aspects of drug development. The increase in (and declining cost of) patient data collection will also drive growth in this field. So I definitely recommend it as a career; quantitatively oriented people are highly valued.

As with any research, the best way to get involved is to dive in at the deep end and join a lab. Courses that offer real-world problems are useful, as well. For example, the biomedical engineering department at Hopkins has introduced a new computing course for freshmen and sophomores that teaches programming, but tests students by having them build and execute simulations for real problems that are created by (and relevant to) the labs of Hopkins faculty. This includes research projects as diverse as object recognition by machines, analysis of genomic information and more