Satoshi Kashiwamura

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This paper presents a hierarchical DNA memory based on nested PCR. Each memory consists of address blocks and a data block. In order to access specific data, we specify the order of the address primers, and nested PCR are performed by using these primers. Our laboratory experiments are also presented to demonstrate the feasibility of the proposed memory.
Simulators for biomolecular computing, (both in vitro and in silico), have come to play an important role in experimentation, analysis, and evaluation of the efficiency and scalability of DNA and biomolecule based computing. Simulation in silico of DNA computing is useful to support DNA-computing algorithm design and to reduce the cost and effort of lab(More)
DNA is an attractive memory unit because of its immense information density. Here, we describe a memory model made of DNA, called Nested Primer Molecular Memory (NPMM). NPMM consists of many DNA strands, and each DNA strand consists of two areas: a data area and a data address area. When the address of target data is specified, only the target data can be(More)
A DNA Memory with over 10 million (16.8 M) addresses was achieved. The data embedded into a unique address was correctly extracted through an addressing processes based on nested PCR. The limitation of the scaling-up of the proposed DNA memory is discussed by using a theoretical model based on combinatorial optimization with some experimental restrictions.(More)
Polymerase Chain Reaction (PCR) is the most important experimental technique in DNA computing. When a concentration of DNA sequence is too small to investigate, PCR amplifies the DNA sequence by the addition of a polymerase. PCR is frequently used in DNA computing, because the calculation result is usually represented by a small concentration of DNA(More)
DNA computing often makes use of annealing or hybridization, whether for vastly generating the initial candidate answers or amplification by using polymerase chain reaction. The main idea behind molecular search or DNA computing approaches for solving weighted graph problems is by controlling the degree of hybridization in order to generate more double(More)
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