Two-gene switches aren’t exactly new to biology, says Roger Brent, associate director of research at the Molecular Sciences Institute in Berkeley, Calif., a nonprofit research firm. Brent-who evaluated biocomputing research for the Defense Advanced Research Projects Agency-says that genetic engineers “have made and used such switches of increasing sophistication since the 1970s. We biologists have tons and tons of cells that exist in two states” and change depending on external inputs.
For Brent, what’s most intriguing about the B.U. researchers’ genetic switch is that it could be just the beginning. “We have two-state cells. What about four-state cells? Is there some good there?” he asks. “Let’s say that you could get a cell that existed in a large number of independent states and there were things happening inside the cell…which caused the cell to go from one state to another in response to different influences,” Brent continues. “Can you perform any meaningful computation? If you had 16 states in a cell and the ability to have the cell communicate with its neighbors, could you do anything with that?”
By itself, a single cell with 16 states couldn’t do much. But combine a billion of these cells and you suddenly have a system with 2 gigabytes of storage. A teaspoon of programmable bacteria could potentially have a million times more memory than today’s largest computers-and potentially billions upon billions of processors. But how would you possibly program such a machine?
Programming is the question that the Amorphous Computing project at MIT is trying to answer. The project’s goal is to develop techniques for building self-assembling systems. Such techniques could allow bacteria in a teaspoon to find their neighbors, organize into a massive parallel-processing computer and set about solving a computationally intensive problem-like cracking an encryption key, factoring a large number or perhaps even predicting weather.
Researchers at MIT have long been interested in methods of computing that employ many small computers, rather than one super-fast one. Such an approach is appealing because it could give computing a boost over the wall that many believe the silicon microprocessor evolution will soon hit. When processors can be shrunk no further, these researchers insist, the only way to achieve faster computation will be by using multiple computers in concert. Many artificial intelligence researchers also believe that it will only be possible to achieve true machine intelligence by using millions of small, connected processors-essentially modeling the connections of neurons in the human brain.