Abstract
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.
Publication types
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Research Support, N.I.H., Extramural
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Research Support, Non-U.S. Gov't
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Base Sequence
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Basic Helix-Loop-Helix Leucine Zipper Transcription Factors / metabolism
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Basic Helix-Loop-Helix Transcription Factors / metabolism*
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Computational Biology
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Computer Simulation
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DNA / metabolism*
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DNA, Fungal / genetics
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DNA, Fungal / metabolism
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DNA-Binding Proteins / metabolism
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E-Box Elements
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Gene Expression Regulation, Fungal
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Helix-Loop-Helix Motifs
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Humans
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Microfluidic Analytical Techniques*
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Oligonucleotide Array Sequence Analysis
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Protein Binding
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Protein Isoforms / metabolism
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Regulatory Sequences, Nucleic Acid
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Repressor Proteins / metabolism
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Saccharomyces cerevisiae / genetics
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Saccharomyces cerevisiae Proteins / metabolism
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Systems Biology*
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Templates, Genetic
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Thermodynamics
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Transcription Factors / metabolism
Substances
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Basic Helix-Loop-Helix Leucine Zipper Transcription Factors
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Basic Helix-Loop-Helix Transcription Factors
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CBF1 protein, S cerevisiae
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DNA, Fungal
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DNA-Binding Proteins
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MNT protein, human
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PHO4 protein, S cerevisiae
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Protein Isoforms
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Repressor Proteins
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Saccharomyces cerevisiae Proteins
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Transcription Factors
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DNA