Contact InformationOffice: MBB 3.148BA
Edward M. Marcottemarcotte@icmb.utexas.edu
Mr. and Mrs. Corbin J. Robertson, Sr. Regents Chair in Molecular Biology
Hollaender Distinguished Postdoctoral Fellow, University of California, Los Angeles, 2000
BS, National Merit Scholar, University of Texas - Austin, 1990
PhD, National Science Foundation predoctoral fellow, University of Texas - Austin, 1995
Fellow, Royal Society of Chemistry, 2012
Fellow, American Association for the Advancement of Science, 2011
Edith and Peter O`Donnell Award in Science, 2008
David and Lucile Packard Fellowship in Science and Engineering, 2002-2007
Camille and Henry Dreyfus New Faculty Award, 2001
Bioinformatics of protein function and interactions
Proteomics and bioinformatics
My group studies the large-scale organization of proteins, essentially trying to reconstruct the ‘wiring diagrams’ of cells by learning how all of the proteins encoded by a genome are associated into functional pathways, systems, and networks. We are interested both in discovering the functions of the proteins as well as in learning the underlying organizational principles of the networks. The work is evenly split between computational and experimental approaches, with the latter tending to be high-throughput functional genomics and proteomics approaches for studying thousands of genes/proteins in parallel.
Bioinformatics for discovering protein function
We've discovered a number of features of genomes that allow us to predict functions for proteins that have never been experimentally characterized. Using these techniques and information from over 30 fully sequenced genomes, we were able to calculate the first genome-wide predictions of protein function, finding very preliminary function for over half the 2,500 uncharacterized genes of yeast. Now, with hundreds of genomes in hand, we're extending these techniques, as well as asking basic questions about the evolution of protein interactions and the evolution of genomes.
Proteomics: High-throughput protein expression and interaction profiling
From work of ours and others, it is apparent that proteins in the cell participate in extended protein interaction networks involving thousands of proteins. We are interested in mapping these networks, measuring their dynamics, and using the networks to predict cell behavior and protein function. In the near term, we are developing mass spectrometry methods to measure absolute protein abundances and high-throughput microscopy methods to measure protein sub-cellular locations and activities, both of which allow us to test and extend the network models. In the long term, we would like to build a catalog of protein, mRNA and metabolite expression from cells grown under many different conditions, forming a quantitative picture of these molecular events inside cells. We expect that data of these sorts will put us on the road to developing predictive, rather than descriptive, theories of biology.
Vogel C, Marcotte EM. "Insights into the regulation of protein abundance from proteomic and transcriptomic analyses" Nature Reviews Genetics 13 (2012):227-32.
Lee I et al. "Prioritizing candidate disease genes by network guilt-by-association of genome-wide association data" Genome Research 21(2011):1109-21.
McGary KL et al. "Systematic discovery of non-obvious human disease models through orthologous phenotypes" Proc. Natl. Acad. Sci. USA 107 (2010):6544-9.
Lee I et al. "Rational association of genes with traits using a genome-scale gene network for Arabidopsis thaliana" Nature Biotechnology 28 (2010):149-156.
Tabor JJ et al. "A Synthetic Genetic Edge Detection Program" Cell 137 (2009): 1272-1281.
Narayanaswamy R et al. "Widespread reorganization of metabolic enzymes into reversible assemblies upon nutrient starvation" Proc. Natl. Acad. Sci. USA 106 (2009): 10147-10152.
Li Z et al. "Rational Extension of the Ribosome Biogenesis Pathway Using Network-Guided Genetics" PLoS Biology (2009): .
Vogel C, Marcotte EM. "Calculating absolute and relative protein abundance from mass spectrometry based protein expression data." Nature Protocols 3 (2008): 144-1451.
Lee I et al. "A single network comprising the majority of genes accurately predicts the phenotypic effects of gene perturbation in C. elegans" Nature Genetics 40 (2008): 181-188.
Lu P et al. "Absolute protein expression profiling estimates the relative contributions of transcriptional and translational regulation" Nature Biotechnology 25 (2007): 117-124.
Levskaya A et al. "Bacterial Photography: Engineering E. coli to See Light" Nature 438 (2005): 441-442.
Lee I et al. "A probabilistic functional network of yeast genes" Science 306 (2004): 1555-1558.