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Computer scientists at Caltech have designed DNA molecules that can carry out reprogrammable computations, for the first time creating so-called algorithmic self-assembly in which the same "hardware" can be configured to run different "software."
In a paper publishing in Nature on March 21, a team headed by Caltech's Erik Winfree (PhD '98), professor of computer science, computation and neural systems, and bioengineering, showed how the DNA computations could execute six-bit algorithms that perform simple tasks. The system is analogous to a computer, but instead of using transistors and diodes, it uses molecules to represent a six-bit binary number (for example, 011001) as input, during computation, and as output. One such algorithm determines whether the number of 1-bits in the input is odd or even, (the example above would be odd, since it has three 1-bits); while another determines whether the input is a palindrome; and yet another generates random numbers.
The system works by self-assembly: small, specially designed DNA strands stick together to build a logic circuit while simultaneously executing the circuit algorithm. Starting with the original six bits that represent the input, the system adds row after row of molecules—progressively running the algorithm. Modern digital electronic computers use electricity flowing through circuits to manipulate information; here, the rows of DNA strands sticking together perform the computation. The end result is a test tube filled with billions of completed algorithms, each one resembling a knitted scarf of DNA, representing a readout of the computation. The pattern on each "scarf" gives you the solution to the algorithm that you were running. The system can be reprogrammed to run a different algorithm by simply selecting a different subset of strands from the roughly 700 that constitute the system.
The researchers were able to experimentally demonstrate six-bit molecular algorithms for a diverse set of tasks. In mathematics, their circuits tested inputs to assess if they were multiples of three, performed equality checks, and counted to 63. Other circuits drew "pictures" on the DNA "scarves," such as a zigzag, a double helix, and irregularly spaced diamonds. Probabilistic behaviors were also demonstrated, including random walks, as well as a clever algorithm (originally developed by computer pioneer John von Neumann) for obtaining a fair 50/50 random choice from a biased coin.
Although DNA computers have the potential to perform more complex computations than the ones featured in the Nature paper, Winfree cautions that one should not expect them to start replacing the standard silicon microchip computers. That is not the point of this research. "These are rudimentary computations, but they have the power to teach us more about how simple molecular processes like self-assembly can encode information and carry out algorithms.
Biology is proof that chemistry is inherently information-based and can store information that can direct algorithmic behavior at the molecular level," he says.
We use this set to construct 21 circuits that execute algorithms including copying, sorting, recognizing palindromes and multiples of 3, random walking, obtaining an unbiased choice from a biased random source, electing a leader, simulating cellular automata, generating deterministic and randomized patterns, and counting to 63, with an overall per-tile error rate of less than 1 in 3,000. These findings suggest that molecular self-assembly could be a reliable algorithmic component within programmable chemical systems.