July 2006, Issue 6

Applying Genomic Data to the Diagnosis and Treatment of Disease

Donna Slonim, PhD, joined the faculty of the Department of Computer Science in the School of Engineering in 2005. She holds a secondary appointment in the Department of Pathology at Tufts University School of Medicine and is a faculty member of the Genetics Program of the Sackler School of Graduate Biomedical Sciences. Slonim works in bioinformatics and computational genomics, whose practitioners use computer and statistical techniques to solve information problems in the life sciences. She uses her expertise in genomic mapping and analysis of gene expression to answer biomedical questions, focusing primarily on applications to cancer and embryonic development.

After earning her PhD in computer science from Massachusetts Institute of Technology, Slonim joined the research staff of the Whitehead Institute/MIT Center for Genome Research (WICGR) as a postdoctoral associate. She worked on algorithms used to build the first large-scale integrated maps of the entire human genome, as well as maps of the mouse and rat genomes. She later joined the Cancer Genomics Group at the WICGR, where she developed methods for analyzing data from gene expression microarray technology. Her work there led to the first paper showing that expression data could be used to classify cancer in a clinically relevant fashion. Slonim most recently worked at Wyeth Research (formerly known as Genetics Institute) in Cambridge, Massachusetts, where she developed novel methods of analyzing gene expression data to support pharmacogenomics, the application of genomics to the discovery and development of drugs.

“My group is focused on developing novel computational methods to advance research in two primary areas: improving our molecular understanding of cancer, and determining key regulatory processes governing normal (and abnormal) embryonic development,” says Slonim. “Ongoing projects include developing better models of metastasis, designing novel approaches to the analysis of microarray data, and inferring regulatory networks by integrating different types of genomic information.”

Genes are segments of DNA that are responsible for the inherited characteristics that distinguish one individual from another. Each human individual has an estimated 25,000 separate genes, and the nucleus of every cell of that individual's body contains a copy of all of her genes. At any given time, a gene can be turned on (expressed), leading to production of its corresponding protein, or it can be turned off. The expression of specific gene subsets in a cell determines its structure, function and behavior.

Slonim develops computational methods to search extensive gene and protein databases for meaningful biological and medical information. Her research often involves finding differences between one data set and another, or patterns within a data set. The advent of DNA microarray (or gene chip) technology has revolutionized genetics research by allowing researchers to quickly examine thousands of genes from a sample of cells and to determine which genes are being expressed and the levels at which they are being expressed. Comparisons between microarrays from normal and diseased cells can reveal crucial information. For example, researchers have already used differences between cells from tumors and cells from normal tissue to provide clues as to what, at the molecular level, causes cancer or determines its destructiveness. A sample of a patient’s cancer cells may also tell whether the patient is likely to respond to a particular treatment. Besides developing and running computer simulations for these kinds of problems, Slonim also works with collaborators to plan specific “wet lab” experiments in order to obtain data that would further the research.

“Probably the most exciting thing I’m working on is biological network inference methods, which are computational methods for inferring (learning) the structure of biological ‘networks,’ such as which genes control the regulation of other genes, or which proteins interact with which other proteins, generally in a context-dependent fashion,” says Slonim. “For example, it is known that most biological networks are ‘scale-free,’ which means that most genes/proteins have only a few neighbors. But a small number of them have a very large number of neighbors. These latter genes are considered to be ‘hubs’ of the network. Different types of hubs tend to regulate different biological processes. Some transcription factors [hubs] are constantly regulating large numbers of targets, but others, termed ‘temporal hubs,’ have a large number of neighbors but only a few at any one time/condition. One question I'd like to answer is whether processes of differentiation, whether of a developing embryo or of a cell progressing to a cancerous state, have these sorts of temporal hubs, and if so, how we can identify them.”
 
Slonim is especially excited about combining these network inference methods with comparative genomics to explore developmental biology. She is looking at biological databases of several model organisms to study gene regulation during the course of development. “I’ve had a longstanding collaboration with a group at Harvard on developmental network control in the worm C. elegans,” says Slonim. “While it’s not a great model for all human systems, it actually is for studying development because it’s literally transparent, so from direct observation we know a lot about how it develops. It’s also very well studied genetically, and easy to work with in the laboratory.” Slonim is also collaborating with Diana Bianchi and Jill Maron, of Tufts University School of Medicine and Tufts–New England Medical Center, who are studying human fetal gene expression in maternal blood. Slonim is working with Bianchi and Maron on the interpretation of their preliminary data, planning future experiments, and possibilities for future grant applications.

Slonim works both with computer science students on the Medford/Somerville campus and with biomedical science students on the Boston campus. She is bringing computation and biomedical science together in a more formal fashion, within her own research group and throughout the university. She also hopes to develop educational paths and programs for undergraduate and graduate study in bioinformatics.  “I’m excited about this,” says Slonim. “I think this is a great opportunity to put together a group that does both things. I think Tufts is a good place to do it because there’s a real interest in this kind of interdisciplinary, intercampus work.”

For more information, please go to http://www.eecs.tufts.edu/~slonim/

 

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