2001 Fiscal Year Final Research Report Summary
A Biomolecular Implementation and Applications of Large Scale Molecular Memories Based on Aqueous Computing
| Project/Area Number |
12480084
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | TOKYO INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
YAMAMURA Masayuki Interdisciplinary Graduate School of Science and Engineering, Tokyo Institute of Technology, Associate Professor, 大学院・総合理工学研究科, 助教授 (00220442)
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| Project Period (FY) |
2000 – 2001
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| Keywords | molecular memory / aqueous computing / DNA computing / peptide nucleicacid / whiplash PCR |
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| Research Abstract |
This project shows a biomolecular implementation and applications of largescale molecular memories based on Aqueous Computing. Aqueous Computing is a kind of DNA computing framework and this project will be a fundamental implementation for intelligent and realistic applications. The research result consists of following two category.
First, we examine the reaction conditions for forming DNA-PNA-PNA triplex by using bis-PNA, that is a special molecule which connects two same PNA sequence by short polymer, and collect basic data. We can avoid extreme low yield that we faced in the first year by accumulating knowledge to synthesize PNA. We have achieved three bit memory which is the same scale by conventional method with natural enzymes.
Second, we try a feasibility study on the memory state copy protocol that we proposed in the first year as an application of whiplash PCR proposed by Hagiya. Whiplash PCR skips the DNA-PNA-PNA triplex so that the bit state of the memory molecule will be copied. Copied memory molecules can be used in the following DNA computing steps because they do not contain any non-natural materials. We have found a phenomenon that skips a simple one bit.
We could not achieve such a large scale memories that can be used in real applications. However, we have found knowledge that leads to build large scale memories for real applications because the first results realize the same scale as conventional methods and the second results will be useful to make hierarchical construction of more large memories.
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