What is your area of focus?
My lab specializes in using biological big data to understand complex human traits, especially those related to the brain and sensory systems. For instance, we are studying data generated by other groups in the HRP that relate to the expression and regulation of genes in the sensory organs of the inner ear. We integrate these data with information about the genetic risk for hearing loss and information about how proteins interact with one another inside cells. In biology, it is often true that the whole is greater than the sum of its parts. So, by putting all of hese data together, we can learn things that wouldn’t be apparent from any one experiment.
Why did you decide to get into scientific research?
I got hooked on scientific research at a pretty early age. I had the good fortune to grow up in Woods Hole, Massachusetts, a “science village” that is home to several world-class oceanographic research institutions. I began working in Woods Hole labs when I was in high school, initially studying the behavior of marine animals.
Why hearing research?
I am new to hearing research, having spent most of my career studying the genetics of behavioral traits and of psychiatric and neurodegenerative disorders, all of which involve changes in gene regulation in specific cell types in the brain or sensory organs. When I joined the University of Maryland School of Medicine, HRP member Ronna Hertzano, M.D., Ph.D., encouraged me to apply my expertise to hearing loss research. Ronna’s team works to identify regulators of gene expression in hair cell development, which complements my work. Conveniently, her lab is located in a building adjacent to the Institute for Genome Sciences, where I am based, and it has been rewarding to work together on a variety of projects related to the genomics of hearing loss. Hearing loss impacts millions of people in the United States and around the world. I’m excited that our research might make a difference.
What is the most exciting part of your research?
Making discoveries! Scientific research is technically and intellectually demanding. We may imagine that discoveries come in a flash. The reality is important insights often come gradually, built up over a variety of distinct experiments.
If you weren’t a scientist, what would you have done?
I would probably have become a professional musician. After graduating from Harvard, I entered a master’s program in cello performance at Boston University, while working about 20 hours a week in a science lab to pay the bills. At the time, my research focused on the neurobiological mechanisms of social behavior in ants. I quickly found that while I loved music, I was even more passionate for science. So I applied for Ph.D. programs in neuroscience. I think I made the right decision, but it’s fun to see what all of my musician friends are doing these days and think about how different my life might have been!
Describe a typical day.
As a computational biologist, most of my research involves computers. I write computer scripts to apply algorithms that we have developed to glean insights from genomic data. These days, most of the work in the lab is performed by graduate and postdoctoral students, so I spend a lot of my time meeting with students to help them design new analyses and interpret their results.
Which mentor do you find the most inspirational?
I’ve been lucky to have terrific mentors throughout my career. In grad school, I worked with Gene Robinson, Ph.D., an amazing scientist who pioneered the use of genomics to study honeybee social behavior. As a postdoc, I worked with Lee Hood, M.D., Ph.D., and Nathan Price, Ph.D. Lee is a truly astounding individual who was a central figure in the Human Genome Project and then helped found the field of systems biology. Nathan has made a number of important advances in computational biology.
How has the collaborative effort helped your research?
Collaborative “big science” approaches are increasingly important in biology. Solving tough, multidisciplinary problems requires teams of scientists with complementary expertise. The HRP is a good example of this.
What do you hope for the HRP over the next few years?
HRP researchers have generated several large datasets over the past few years, which describe molecular changes in multiple models of hair cell development and regeneration. Our challenge over the next one to two years is to integrate these data to identify actionable insights in the form of specific genes that are capable of driving regeneration.