Curtis Huttenhower, Ph.D.
|Title||Associate Professor of Computational Biology and Bioinformatics|
|Institution||Harvard School of Public Health|
|Address||Harvard School of Public Health|
655 Huntington Ave
Boston MA 02115
Dr. Huttenhower's research is concerned with the discovery of useful biological knowledge in large collections of genomic data. Modern biological experiments each represent a detailed snapshot of a cell or organism's internal state, and public repositories already contain many thousands of experimental results and are constantly growing in size and diversity. Taken together, these data can be used to reconstruct detailed models of cellular behavior in response to changing environmental conditions, regulatory and metabolic regimes, and disease states. The goal of this research is to allow any new biomedical question to be answered by extracting information from the entire body of existing and novel experimental data, using data integration to allow results from basic research to be applied to genomic and personalized medicine (and vice versa).
In practice, this requires the development of computational methodology that is efficient enough to deal with billions of data points while remaining biologically rich enough to capture the complexities of metazoan molecular biology. This can include techniques from statistical machine learning, graphical models, and information retrieval, applied to biological systems as diverse as growth control in yeast or survival mechanisms in human fibroblasts. Many results also rely on the construction and analysis of biological networks, including physical protein-protein interactions, regulatory networks, or functional associations among genes and gene products. The challenge is not only to develop useful computational models, but also to apply them collaboratively to drive novel experimental biology and to better understand biomedical results.
Local representatives can answer questions about the Profiles website or help with editing a profile or issues with profile data. For assistance with this profile: SPH faculty should contact Justin Sayde (jsaydehsph.harvard.edu).
Click the "See All" links for more information and interactive visualizations!
People who are also in this person's primary department.