DNA-based Learning Algorithms and their Applications

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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.