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Science 12 January 2007:
Vol. 315. no. 5809, pp. 233 - 237
DOI: 10.1126/science.1131007
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Reports

A Systems Approach to Measuring the Binding Energy Landscapes of Transcription Factors

Sebastian J. Maerkl1,2 and Stephen R. Quake2*

A major goal of systems biology is to predict the function of biological networks. Although network topologies have been successfully determined in many cases, the quantitative parameters governing these networks generally have not. Measuring affinities of molecular interactions in high-throughput format remains problematic, especially for transient and low-affinity interactions. We describe a high-throughput microfluidic platform that measures such properties on the basis of mechanical trapping of molecular interactions. With this platform we characterized DNA binding energy landscapes for four eukaryotic transcription factors; these landscapes were used to test basic assumptions about transcription factor binding and to predict their in vivo function.

1 Biochemistry and Molecular Biophysics Option, California Institute of Technology, 1200 East California Boulevard, Pasadena, CA 91125, USA.
2 Department of Bioengineering, Stanford University and Howard Hughes Medical Institute, 318 Campus Drive, Stanford, CA 94305, USA.

* To whom correspondence should be addressed. E-mail: quake{at}stanford.edu

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THIS ARTICLE HAS BEEN CITED BY OTHER ARTICLES:
Diversity and Complexity in DNA Recognition by Transcription Factors.
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An affinity-based scoring scheme for predicting DNA-binding activities of modularly assembled zinc-finger proteins.
J. D. Sander, P. Zaback, J. K. Joung, D. F. Voytas, and D. Dobbs (2009)
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Experimental advances in the characterization of metazoan gene regulatory networks.
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Brief Funct Genomic Proteomic 8, 12-27
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Predicting transcription factor specificity with all-atom models.
S. Jamal Rahi, P. Virnau, L. A. Mirny, and M. Kardar (2008)
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A high-throughput percentage-of-binding strategy to measure binding energies in DNA-protein interactions: application to genome-scale site discovery.
X. Wang, H. Gao, Y. Shen, G. M. Weinstock, J. Zhou, and T. Palzkill (2008)
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Energy-dependent fitness: A quantitative model for the evolution of yeast transcription factor binding sites.
V. Mustonen, J. Kinney, C. G. Callan Jr, and M. Lassig (2008)
PNAS 105, 12376-12381
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Nearest-neighbor non-additivity versus long-range non-additivity in TATA-box structure and its implications for TBP-binding mechanism.
H. Faiger, M. Ivanchenko, and T. E. Haran (2007)
Nucleic Acids Res. 35, 4409-4419
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