Edward M. Marcotte
教授
Center for Systems and Synthetic Biology
The University of Texas at Austin;Department of Molecular Biosciences, College of Natural Sciences, The University of Texas at Austin;Institute for Cellular & Molecular Biology, College of Natural Sciences, The University of Texas at Austin;Erisyon
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基本信息
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个人简介
Research Interests
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 experimental and computational approaches, with the former 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 thousands 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.
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 experimental and computational approaches, with the former 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 thousands 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.
研究兴趣
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Riddhiman K. Garge,Renee C. Geck, Joseph O. Armstrong,Barbara Dunn,Daniel R. Boutz,Anna Battenhouse,Mario Leutert,Vy Dang,Pengyao Jiang, Dusan Kwiatkowski, Thorin Peiser, Hoyt Mcelroy,
Journal of Biological Chemistryno. 3 (2024)
Journal of Biological Chemistryno. 3 (2024)
Muyoung Lee, Qingqing Guo,Mijeong Kim, Joonhyuk Choi, Alia Segura, Alper Genceroglu,Lucy LeBlanc, Nereida Ramirez, Yu Jin Jang, Yeejin Jang,Bum-Kyu Lee,Edward M Marcotte,
Genome research (2024)
bioRxiv : the preprint server for biology (2024)
Shintaroh Kubo,Corbin S. Black,Ewa Joachimiak,Shun Kai Yang,Thibault Legal,Katya Peri,Ahmad Abdelzaher Zaki Khalifa,Avrin Ghanaeian,Caitlyn L. McCafferty,Melissa Valente-Paterno, Chelsea De Bellis, Phuong M. Huynh,
biorxiv(2023)
Zenodo (CERN European Organization for Nuclear Research) (2023)
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