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To address these challenges, this dissertation describes a novel design for DNA computation called a localized hybridization network, where diffusion of DNA strands does not occur. Instead all of the DNA strands are localized by attaching them to an addressable substrate such as DNA nanotrack and DNA origami. This localization increases the relative concentration of the reacting DNA strands thereby speeding up the kinetics. This dissertation demonstrated a localized hybridization network that executed a chain reaction of five DNA hybridizations which executes faster than non-localized DNA reactions.
Another advantage of this approach is that each copy of the localized hybridization network operates independently of each other, allowing for a high level of parallelism. Localized hybridization networks also allow one to reuse the same DNA sequence to perform different actions at distinct location on the addressable substrate, increasing the scalability of such systems by exploiting the limited sequence space. An advantage of localized hybridization computational circuit is sharper switching behavior as information is encoded over the state of a single molecule. This also eliminates the need for thresholding as computation is performed locally eliminating the need for a global consensus.
There are many applications for localized hybridization networks. These include counting the number of disease marker molecules in a patient, detecting various cancer DNA sequences, and detecting and distinguishing bacteria by their distinguishing DNA. The results from localized DNA hybridization reactions may also be of practical use in performing surface computation on cellular membranes for disease detection and prevention.