A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge

被引:0
|
作者
Lee, Ju Seok [1 ,2 ,3 ]
Chen, Junghuei [4 ]
Deaton, Russell [5 ]
Kim, Jin-Woo [1 ,2 ,3 ]
机构
[1] Univ Arkansas, Inst Nanosci & Engn, Bio Nano Technol Lab, Fayetteville, AR 72701 USA
[2] Univ Arkansas, Dept Biol & Agr Engn, Fayetteville, AR 72701 USA
[3] Univ Arkansas, Cell & Mol Biol Grad Program, Fayetteville, AR 72701 USA
[4] Univ Delaware, Dept Chem & Biochem, Newark, DE 19716 USA
[5] Univ Memphis, Dept Elect & Comp Engn, Memphis, TN 38117 USA
来源
基金
美国国家科学基金会;
关键词
Biological memory protocol; In vitro learning and recall; Microarray; Genomic status; Ecological and environmental monitoring; Biological and biomedical sensing; MILLIONS;
D O I
10.1186/1754-1611-8-25
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: Genetic material extracted from in situ microbial communities has high promise as an indicator of biological system status. However, the challenge is to access genomic information from all organisms at the population or community scale to monitor the biosystem's state. Hence, there is a need for a better diagnostic tool that provides a holistic view of a biosystem's genomic status. Here, we introduce an in vitro methodology for genomic pattern classification of biological samples that taps large amounts of genetic information from all genes present and uses that information to detect changes in genomic patterns and classify them. Results: We developed a biosensing protocol, termed Biological Memory, that has in vitro computational capabilities to "learn" and "store" genomic sequence information directly from genomic samples without knowledge of their explicit sequences, and that discovers differences in vitro between previously unknown inputs and learned memory molecules. The Memory protocol was designed and optimized based upon (1) common in vitro recombinant DNA operations using 20-base random probes, including polymerization, nuclease digestion, and magnetic bead separation, to capture a snapshot of the genomic state of a biological sample as a DNA memory and (2) the thermal stability of DNA duplexes between new input and the memory to detect similarities and differences. For efficient read out, a microarray was used as an output method. When the microarray-based Memory protocol was implemented to test its capability and sensitivity using genomic DNA from two model bacterial strains, i.e., Escherichia coli K12 and Bacillus subtilis, results indicate that the Memory protocol can "learn" input DNA, "recall" similar DNA, differentiate between dissimilar DNA, and detect relatively small concentration differences in samples. Conclusions: This study demonstrated not only the in vitro information processing capabilities of DNA, but also its promise as a genomic pattern classifier that could access information from all organisms in a biological system without explicit genomic information. The Memory protocol has high potential for many applications, including in situ biomonitoring of ecosystems, screening for diseases, biosensing of pathological features in water and food supplies, and non-biological information processing of memory devices, among many.
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页数:12
相关论文
共 3 条
  • [1] A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge
    Ju Seok Lee
    Junghuei Chen
    Russell Deaton
    Jin-Woo Kim
    [J]. Journal of Biological Engineering, 8
  • [2] A DNA-based memory with in vitro learning and associative recall
    Chen J.
    Deaton R.
    Wang Y.-Z.
    [J]. Natural Computing, 2005, 4 (2) : 83 - 101
  • [3] A DNA-based memory with in vitro learning and associative recall
    Chen, JH
    Deaton, R
    Wang, YZ
    [J]. DNA COMPUTING, 2004, 2943 : 145 - 156