2003 Fiscal Year Final Research Report Summary
DNA-based Learning Algorithms and their Applications
| Project/Area Number |
13680464
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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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 | Keio University (2002-2003)
Tokyo Denki University (2001) |
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Principal Investigator |
SAKAKIBARA Yasubumi Keio University, Faculty of Science and Technology, Associate Professor, 理工学部, 助教授 (10287427)
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| Project Period (FY) |
2001 – 2003
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| Keywords | DNA Computer / Molecular Computing / Machine Learning / Biomolecule / Massive Parallel / Biotechnology / Boolean Function / Automaton |
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| Research Abstract |
We have considered a probabilistic interpretation of the test tube which contains a large amount of DNA strands, and proposed a population computation using a number of DNA strands in the test tube and a probabilistic logical inference based on the probabilistic interpretation.
We have applied a DNA-based massively parallel exhaustive search to solving the computational learning problems of DNF Boolean formulae. We propose new methods to encode any k-term DNF formula to a DNA strand, evaluate the encoded DNF formula for a truth value assignment by using hybridization and primer extension with DNA polymerase, and find a consistent DNF formula with the given examples. By employing these methods, we have shown that the class of k-term DNF formulae and the class of general DNF formulae are efficiently learnable on DNA computer.
We have employed the translation mechanism combined with four-base codon techniques to develop a molecular machine which computes finite automata (finite-state machine). We have reported some experimental results where we have succeeded to implement a finite automaton on an E.coli in vitro translation system with four-base codons.
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