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David Gifford, Ph.D.

Department of Electrical Engineering and Computer Science
Professor of Computer Science and Engineering

Room 32-G542
617-253-6039 (phone)

Biosketch

Ph.D. Electrical Engineering 1981 Stanford University

Research Summary

The effort of Gifford™s group in computational genomics focuses on developing computational methods for the analysis of data from high-throughput molecular biology experiments. Advanced technologies that study organisms at the genomic level have created large amounts of information derived from a broad range of formats. Availability of this vast body of information requires new computational approaches for data analysis. The Gifford research group develops methods for integrating data from different formats, such as expression profiling and DNA-binding data, to create quantitative models of complex biological phenomena.

Expression profiling uses DNA microarray technology to understand expression of genes based on genomic knowledge. Microarray research uses levels of messenger RNA (mRNA) expression as a measure of gene expression on a genome-wide scale. Because this technology can interrogate large numbers of genes, expression profiling reveals quantitative, system-level information. However, the challenge remains to distill the results into an interpretable computational form from which a predictive model can be established. The Gifford group is developing computational methods to visualize microarray data in a graphical model and to incorporate other types of data into a predictive model. These methods will enable computational assembly of the collective knowledge into a cohesive picture of cellular and organism function.

Establishing genetic regulatory modules
In collaboration with the Young lab, the Gifford group developed an algorithm, called GRAM (Genetic Regulatory Modules) that incorporates two different data sets to establish gene regulatory pathways. A gene module is defined as a set of co-expressed genes to which the same set of regulatory factors binds. GRAM uses the information from microarray experiments in combination with protein-DNA binding data to describe a genome-wide regulatory network.

The GRAM algorithm performs an efficient, yet exhaustive search of all possible combinations of transcriptional regulators (DNA-binding factors) to genes expressed. Initially, a set of genes is identified to which a common set of transcription regulators binds with stringent affinity. Then the algorithm selects a subset of those genes that have similar expression levels as indicated from the microarray data. This gene module is then further refined to include additional genes with less stringent binding affinities. Through this process, two different data types are integrated to establish gene modules that represent a genome-wide regulatory network for transcription.

Using yeast (Saccharomyces cerevisiae), the Gifford group has applied this new algorithm to 14 transcription factors responsive to rapamycin, a drug given to post-operative transplant patients, and 22 expression experiments. The study identified 39 gene modules that included 317 genes regulated by 13 transcription factors. The GRAM algorithm identified 192 new genes that may be involved in the response to this drug. None of these genes had been implicated in rapamycin response using conventional methods.

Selected Publications

  • "Transcriptional regulatory code of a eukaryotic genome". C. Harbison, D.B. Gordon, T. I Lee, N. J. Rinaldi, K. D. MacIsaac, T. W. Danford, N. M. Hannett, J.B. Tagne, D. B. Reynolds, J. Yoo, E. G. Jennings, J. Zeitlinger, D. K. Pokholok, M. Kellis, P. A. Rolfe, K. T. Takusagawa, E. S. Lander, D. K. Gifford, E. Fraenkel, and R. A. Young. Nature, 431:99-104, September, 2004.
  • "Control of Pancreas and Liver Gene Expression by HNF Transcription Factors". Odom, D. T., Zizlsperger, N., Gordon, D. B., Bell, G. W., Rinaldi, N. J., Murray, H. L., Volkert, T. L., Schreiber, J., Rolfe, P. A., Gifford, D. K., Fraenkel, E., Bell, G. I., Young, R. A. Science, 303:1378-1381, February, 2004.
  • "Negative Information for Motif Discovery." Takusagawa, K. T., Gifford, D. K. Pacific Symposium on Biocomputing 9:360-371 (2004).
  • "Computational discovery of gene modules and regulatory networks". Bar-Joseph, Z., Gerber, G. K., Lee, T. I., Rinaldi, N. J., Yoo, J. Y., Robert, F., Gordon, D. B., Fraenkel, E., Jaakkola, T. S., Young, R. A., Gfford D. K. Nature Biotechnology, 21, pp. 1337-1342 (November, 2003).
  • "Continuous Representations of Time Series Gene Expression Data". Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola and I. Simon. Journal of Computational Biology, 10(3-4) pp. 241-256 (2003).
  • œK-ary Clustering with Optimal Leaf Ordering for Gene Expression Data.? Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi S. Jaakkola and Nathan Srebro. To appear in Proceedings of the 2nd Workshop on Algorithms in Bioinformatics (WABI 2002), Rome, Italy, September 17-21.
  • œCombining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models.? Alexander J. Hartemink, David K. Gifford, Tommi S. Jaakkola, and Richard A. Young. Pacific Symposium on Biocomputing 2002, Kauai, January 2002.
  • new approach to analyzing gene expression time series data.? Z. Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola and I. Simon. In Proceedings of The Sixth Annual International Conference on Research in Computational Molecular Biology (RECOMB), 2002, pp 39-48.
  • œBayesian Methods for Elucidating Genetic Regulatory Networks.? Hartemink, A. J., Gifford, D. K., Jaakkola, T. S., Young, R. Aœ? IEEE Intelligent Systems in Biology, Vol. 17, No. 2, pp. 37-43.
  • œSerial Regulation of Transcriptional Regulators in the Yeast Cell Cycle? Simon, I., Barnett, J., Hannett, N., Harbison, C. T., Rinaldi, N. J., Volkert, T. L. Wyrick, J. J., Zeitlinger, J., Gifford., D. K., Jaakkola, T. S., Young, R. A., Cell, 106, Sept., 2001, p. 667-708.

Last Updated: April 16, 2008