Demonstration of DNA-based semantic model by using parallel overlap assembly

Abstract

In this paper, we propose a novel approach to DNA computing-inspired semantic model. The model is theoretically proposed and constructed with DNA molecules. The preliminary experiment on construction of the small test model was successfully done by using very simple techniques: parallel overlap assembly (POA) method, polymerase chain reaction (PCR), and gel electrophoresis. This model, referred to as 'semantic model based on molecular computing' (SMC) has the structure of a graph formed by the set of all (attribute, attribute values) pairs contained in the set of represented objects, plus a tag node for each object. Each path in the network, from an initial object-representing tag node to a terminal node represents the object named on the tag. Input of a set of input strands will result in the formation of object-representing dsDNAs via parallel self-assembly, from encoded ssDNAs representing both attributes and attribute values (nodes), as directed by ssDNA splinting strands representing relations (edges) in the network. The proposed model is very suitable for knowledge representation in order to store vast amount of information with high density.