Atul Butte speaking at the Van Andel Research Institute

Han-Mo Koo Memorial Seminar Series
Atul Butte, M.D., Ph.D.
Assistant Professor of Medicine (Medical Informatics) and Pediatrics
Stanford University School of Medicine

http://www.vai.org/News/Seminars.aspx

Exploring Genomic Medicine Using Translational Bioinformatics

Atul Butte, M.D., Ph.D. is an Assistant Professor in Medicine (Medical Informatics) and Pediatrics, and by courtesy, Computer Science, at Stanford University and the Lucile Packard Children’s Hospital, and is a board-certified pediatric endocrinologist. Dr. Butte received his undergraduate degree in Computer Science from Brown University in 1991, and worked in several stints as a software engineer at Apple Computer (on the System 7 team) and Microsoft Corporation (on the Excel team). He graduated from the Brown University School of Medicine in 1995, during which he worked as a research fellow at NIDDK through the Howard Hughes/NIH Research Scholars Program. He completed his residency in Pediatrics and Fellowship in Pediatric Endocrinology in 2001, both at Children’s Hospital, Boston. Dr. Butte received a Ph.D. in Health Sciences and Technology from the Medical Engineering / Medical Physics Program in the Division of Health Sciences and Technology, at Harvard Medical School and Massachusetts Institute of Technology.

Dr. Butte’s laboratory focuses on solving problems relevant to genomic medicine by developing new methodologies in translational bioinformatics. Dr. Butte has authored more than 25 publications in bioinformatics, medical informatics, and molecular diabetes, and has delivered more than 65 presentations world-wide on bioinformatics, including 11 at the National Institutes of Health or NIH-sponsored meetings. Along with Isaac Kohane and Alvin Kho, Dr. Butte has co-authored one of the first books on microarray analysis titled “Microarrays for an Integrative Genomics” published by MIT Press. Dr. Butte’s recent awards include the 2007 Genome Technology “Tomorrow’s Principal Investigator” Award, the 2006 Howard Hughes Medical Institute Early Career Award, the 2006 PhRMA Foundation Research Starter Grant in Informatics, the 2002 and 2003 American Association for Clinical Chemistry Outstanding Speaker Award, and the 2001 Lawson Wilkins Pediatric Endocrine Society Clinical Scholar Award. Dr. Butte’s research is supported by grants from the Howard Hughes Medical Institute, the National Library of Medicine, the National Institute for General Medical Science, the National Human Genome Research Institute, the National Cancer Institute, the Pharmaceutical Research and Manufacturers of America Foundation, and the California Institute for Regenerative Medicine.

With the end of the United States NIH budget doubling and completion of the Human Genome Project, there is a need to translate genome-era discoveries into clinical utility. The difficulties in making bench-to-bedside translations have been described: comprehensive molecular studies on patients are expensive, and hospitals are not phenotypers. The nascent field of translational bioinformatics may help. I will highlight three recent translational bioinformatics projects from the laboratory with direct implications for medicine. (1) Nearly 100 years ago, Johannsen proposed the “equation” that phenotype is secondary to both genes and environment. I will show the methods we have constructed that take advantage of the enormous amount of publicly-available genomic data to enable a first-step towards solving Johannsen’s “equation” for all genes, environmental factors, and phenotypes. (2) Modern day use of DNA sequencing has enabled the re-organization of species in the taxonomical trees that date back to Linnaeus. But Linnaeus was also among the first to suggest a taxonomical classification for diseases, or nosology. Sufficient genomic data now exists for us to consider building the first genomic-data driven nosology. I will show how such a nosology enables the discovery of new biomarkers for disease and suggests novel roles for drugs in the treatment of disease. (3) Our lab has successfully built novel “clinical microarrays” from quantitative electronic medical record data on 5,200 patients over 7 years. We have compared these clinical microarrays with our genomic data-driven nosology relating the molecular and clinical side of 12 diseases, which has enabled us to predict a novel interleukin regulator of T-cell development.