Beyond the Identification of Transcribed Sequences: Functional and Expression Analysis

9th Annual Workshop, October 28-31, 1999

Co-sponsored by the U.S. Department of Energy


Expression profiling and genetic networks

R. Somogyi, S. Fuhrman, X. Wen, P. D'haeseleer*, S. Liang, and J.F. Loring

Incyte Pharmaceuticals Inc., Palo Alto, California, USA
*University of New Mexico, Department of Computer Science, Albuquerque, New Mexico, USA

Biological information is transmitted from gene sequence to gene activity patterns. This information feeds back to the regulation of gene expression through a system of inter- and intracellular signaling functions. We have conducted extensive surveys of the dynamics of gene expression to capture essential information flow in genetic signaling networks. Cluster analysis of these data suggests conserved modules of genetic programs and interlinked pathways. Ultimately we seek to build truly predictive models by applying reverse engineering approaches to these data. Using a linear method we have identified several plausible causal links between genes. The knowledge gained through these integrated experimental/computational methods will be critical to therapeutic target discovery and validation, and bioengineering in general.


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