Optimizing Cross-reactivity with Evolutionary Search for Sensors.

We report a straightforward evolutionary procedure to build an optimal sensor array from a pool of DNA sequences oriented toward three-way junctions. The individual sensors were mined from this pool under separate selection pressures to interact with four steroids, while allowing cross-reactivity, i...

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Publicado en:Journal of the American Chemical Society Vol. 134; no. 3; pp. 1642 - 1648
Autores principales: Kyung-Ae Yang, Renjun Pei, Stefanovic, Darko, Stojanovic, Milan N.
Formato: Artículo
Publicado: American Chemical Society 1/25/2012
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 1/25/2012
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      pub: American Chemical Society
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        10.1021/ja2084256
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        atl: Optimizing Cross-reactivity with Evolutionary Search for Sensors.
      aug:
        au:
          Kyung-Ae Yang
          Renjun Pei
          Stefanovic, Darko
          Stojanovic, Milan N.
        affil:
          Division of Experimental Therapeutics, Department of Medicine, Columbia University, New York, New York 10032, United States
          Department of Computer Science and Center for Biomedical Engineering, University of New Mexico, Albuquerque, New Mexico 87131, United States
          Department of Biomedical Engineering, Columbia University, New York, New York 10032, United States
      su:
        Oligonucleotide arrays
        Detectors
        Nucleotide sequence
        Steroids
        Hydrophobic compounds
        Oligonucleotide synthesis
      sug:
        subj:
          Oligonucleotide arrays
          Detectors
          Nucleotide sequence
          Steroids
          Hydrophobic compounds
          Oligonucleotide synthesis
      ab: We report a straightforward evolutionary procedure to build an optimal sensor array from a pool of DNA sequences oriented toward three-way junctions. The individual sensors were mined from this pool under separate selection pressures to interact with four steroids, while allowing cross-reactivity, in a manner designed to achieve perfect classification of individual steroids. The resulting sensor array had three sensors and displayed discriminatory capacity between steroid classes over full ranges of concentrations. We propose that similar protocols can be used whenever we have two or more classes of samples, with individual classes being defined through gross differences in ratios of dominant families of responsive components.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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