Imagine Magazine - Johns Hopkins - September/October 2010 - (Page 38)

exploring career options Computational Biologist Aravinda Chakravarti, PhD Professor of Medicine, Pediatrics, Molecular Biology & Genetics Johns Hopkins University School of Medicine Professor of Biostatistics McKusick-Nathans Institute of Genetic Medicine Aravinda Chakravarti was getting his undergraduate degree in math and statistics when he asked a biology professor to teach him molecular biology on the side. Twenty-ﬁve years later, he found himself on the advisory council of the National Human Genome Research Institute, which carried out the NIH’s role in the International Human Genome Project, a massive undertaking that sequenced the three billion base pairs in human DNA. Today, he uses his quantitative skills to perform computational analyses on vast biological datasets to discover how genes interact with each other and the environment to cause diseases ranging from heart disorders and diabetes to mental illness and autism. Here, he explains his work and what he thinks is next in his rapidly changing ﬁeld. Interview by Kristi Birch What is computational biology? How is it different from bioinformatics? Bioinformatics is the science of the storage, retrieval, management, and statistical analysis of large datasets in the biological sciences. Computational biology is computation on those datasets to draw biological inferences. Physicists compute, financial people compute, sociologists compute, as do we biologists. The important difference is the domain of knowledge, the principles of biology, not the hardware. To be a computational biologist, you have to be a biologist in your heart and in your soul. The importance of both of these areas has increased exponentially in the past few years with our ability to examine the genome in a variety of ways—by sequencing the genome or assessing the genetic activity of each gene at the RNA level or understanding gene regulation by methylation—in many individuals, both with and without disease. In your lab, you look for genes implicated in certain diseases. How do you use computational biology to do that? We examine the genomes of hundreds to thousands of people to identify which genes are enriched or deficient in patient groups compared with controls. We study not only the structural differences (the presence or absence of a specific gene and the number of copies of each) among genomes, but also the impact these structural differences have on gene function. We use both bioinformatics tools and computational biology methods to search for those differences between individuals at the DNA and RNA levels. Bioinformatics looks at huge volumes of data to distill its features in some essential ways. Computational biology requires us to search these data against a genetic model of disease so that relevant inferences can be made. We used to do these studies one gene at a time. Now we do them an entire genome at a time. Pretty soon we will be doing these experiments at the level of individual DNA nucleotides. This increase in resolution has not only led to the boon in biological discoveries but changed the way we do science. In the past, most of our time was spent on doing data collection and little on interpretation. Now, it’s the reverse. We spend days or weeks just gathering the data and then weeks or months doing intense analyses of the large data set. Often, we have to invent new analytical methods and tools, since the path is still not well trodden. New technologies (both genomic and computational) that allow us to sequence entire genomes at a much lower cost, or look at functional changes in gene activity, have rapidly altered what we can do in human disease discovery. At the beginning of the Human Genome Project, some scientists said we would find nothing, and others said it would be boring or meaningless. It has been anything but. We find new treasures of biology every day. 38 imagine Sept/Oct 2010 iStock

Table of Contents for the Digital Edition of Imagine Magazine - Johns Hopkins - September/October 2010

Imagine Magazine - John Hopkins - September/October 2010
Contents
Big Questions
Big Picture
In My Own Words
Biotechnology is
A Survival Guide for the Biotech Revolution
The Indiana Jones of Biotech
Stemming the Monster
Transgenic Animals
Building a Better Hydrogel
Immortal Cells & Bioethics
Selected Opportunities & Resources
Nights at the Museum
One Step Ahead
Middle Ground
Off the Shelf
Word Wise
Exploring Career Options
Planning Ahead for College
Students Review
Creative Minds Imagine
Mark Your Calendar
Knossos Games

Imagine Magazine - Johns Hopkins - September/October 2010