DNA Hypernetworks for Information Storage and Retrieval
Content-addressability is a fundamental feature of human memory underlying many associative information retrieval tasks. In contrast to location-based memory devices, content-addressable memories require complex interactions between memory elements, which makes conventional computation paradigms difficult. Here we present a molecular computational model of content-addressable information storage and retrieval which makes use of the massive interaction capability of DNA molecules in a reaction chamber. This model is based on the “hypernetwork” architecture which is an undirected hypergraph of weighted edges. We describe the theoretical basis of the hypernetwork model of associative memory and its realization in DNA-based computing. A molecular algorithm is derived for automatic storage of data into the hypernetwork, and its performance is examined on an image data set. In particular, we study the effect of the hyperedge cardinality and error tolerance on the associative recall performance. Our simulation results demonstrate that short DNA strands in a vast number can be effective in some pattern information processing tasks whose implementation is within reach of current DNA nanotechnology.
KeywordsAssociative Memory Incidence Matrix Error Tolerance Pattern Completion Ordinary Graph
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