Title: Assistant Professor of Computational Biology and Bioinformatics, Harvard University
Education: PhD, Princeton University, 2008
Recommended by: Winston Hide, John Quackenbush
If it were possible to deliberately design an educational and experiential program that would produce a highly qualified computational biologist, the end result might look a lot like Curtis Huttenhower's CV.
Huttenhower began by majoring in computer science, chemistry, and math — a good start, to be sure. But the real kickers were a stint as a software design engineer at Microsoft and a master's degree in language technologies at Carnegie Mellon, during which he was introduced to computational biology. He went on to earn another master's and a PhD in computer science at Princeton, where he also served as a postdoctoral researcher for Olga Troyanskaya.
All of that has led to someone with skills in natural language processing and computer science and a serious interest in biology and chemistry. As an assistant professor at Harvard's School of Public Health, those attributes come in handy. "My overall interest is really in using all available experimental data to answer specific biological questions," Huttenhower says. He's spent the first few months of his new gig steeping himself in the biological work going on around him at Harvard, where he's found that metagenomics — and studying the human microbiome in particular — has piqued his interest. "It's really fascinating since it's been known for a long time that there's a tremendous number of microorganisms living there, especially in the gut, and they have a huge impact on human health," he says. He is starting collaborations that will help get a handle on the challenge of taking reams of sequence data and integrating that with functional data from other systems to figure out "what is the community of organisms in your gut doing to influence health," he says.
In other work, Huttenhower is teaming up with colleagues at the Dana-Farber Cancer Institute to make the most of large-scale cohort studies that are moving into the genomic era, he says. He's focusing on a project targeting colon cancer that is starting to perform genome-wide gene expression studies on patients. "It's a great data integration opportunity," Huttenhower says. "How can you tie that to the greater body of genomic data that's out there?"
Huttenhower says he's especially interested in what he sees as "a big opportunity in this particular area" — that is, "the chance to really integrate computational work with molecular work [and] with clinical work." Whether it's known as translational medicine or personalized medicine, he believes that in the near future a lot of this work will come together.
Publications of note
To get a sense of what Huttenhower's been working on, check out "Exploring the human genome with functional maps," a Genome Research paper that came out this year on which he's lead author. The focus of the study is on using functional maps to summarize data and pathway interactions. "Using a regularized Bayesian integration system, we provide maps of functional activity and interaction networks in over 200 areas of human cellular biology, each including information from approximately 30,000 genome-scale experiments pertaining to approximately 25,000 human genes," the authors write in the abstract. "In addition to providing maps of each of these areas, we also identify biological processes active in each data set."
And the Nobel goes to...
If Huttenhower were to receive that fateful call, he would like it to be for finding a way "to automatically, quickly, and easily take new data and drop it into a system and get back recommendations for personalized medicine, automated diagnoses, and advice in the clinic," he says. His dream is to "incorporate all the sorts of things that people are looking at now in terms of drug interactions [and] genomics" and making that work in a simple, clinically useful tool.