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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.    Electronic supplementary material  The online version of this article (doi:10.1186/1754-1611-8-25) contains supplementary material, which is available to authorized users.

journal of biological engineering

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