title: A DNA-based pattern classifier with in vitro learning and associative recall for genomic characterization and biosensing without explicit sequence knowledge.
abstract: 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.
authors.affiliation: Bio/Nano Technology Laboratory, Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Cell and Molecular Biology Graduate Program, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Department of Chemistry, Seoul National University, Seoul, Republic of Korea.
authors.forename: Ju Seok
authors.initials: JS
authors.lastname: Lee
authors.affiliation: Department of Chemistry and Biochemistry, University of Delaware, Newark, Delaware 19716 USA.
authors.forename: Junghuei
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, University of Memphis, Memphis, Tennessee 38117 USA.
authors.forename: Russell
authors.initials: R
authors.lastname: Deaton
authors.affiliation: Bio/Nano Technology Laboratory, Institute for Nanoscience and Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Department of Biological and Agricultural Engineering, University of Arkansas, Fayetteville, Arkansas 72701 USA ; Cell and Molecular Biology Graduate Program, University of Arkansas, Fayetteville, Arkansas 72701 USA.
authors.forename: Jin-Woo
authors.initials: JW
authors.lastname: Kim
keywords: Biological and biomedical sensing; Biological memory protocol; Ecological and environmental monitoring; Genomic status; In vitro learning and recall; Microarray