In 1994, in a Los Angeles lab, a mathematician tired of solving abstract problems picked up a biology book. As he learned about DNA, he noticed how much it resembled the logic of computers, and had an idea. His name was Leonard Adleman, and he would go on to invent the world’s first DNA computer.
For his proof of concept, Adleman chose the Hamiltonian path problem, a classic of computer science: given a set of cities and routes, is there a way to visit each city exactly once? Traditional computers would take ages to brute-force such a problem, but DNA, with its staggering capacity for parallelism, was a perfect fit. Adleman encoded the cities into short DNA sequences, mixed them in solution, and let nature’s chemistry do the heavy lifting.
Within seconds, the DNA strands had combined in countless ways, effectively testing every possible route at once. The computation ran hundreds of times faster than the best computer of the era, while being billions of times more energy-efficient. His experiment birthed the field of “DNA computing,” sparking visions of machines no bigger than a drop of water yet capable of solving problems beyond the reach of silicon.
DNA computing never went mainstream, largely because scaling and generalizing these experiments proved too difficult and silicon chips rapidly caught up. But Adleman’s breakthrough offered a striking glimpse of alternative ways to compute, reminding us that living systems themselves are extraordinary information processors.
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