Meet the compact, energy-efficient bio-powered supercomputer prototype

TORONTO: Scientists have created an energy efficient model biological supercomputer that can process information very quickly and accurately using parallel networks in the same way that massive electronic computers do.

The model bio-supercomputer is much smaller than current supercomputers, uses much less energy, and uses proteins present in all living cells to function, researchers said.

The substance that provides energy to all the cells in our bodies, Adenosine triphosphate (ATP), may also be able to power the next generation of supercomputers, they said.

"We've managed to create a very complex network in a very small area," said Dan Nicolau from the McGill University in Canada.

The model bio-supercomputer is a result of a combination of geometrical modelling and engineering know-how on the nano scale. It is a first step, in showing that this kind of biological supercomputer can actually work.

The circuit in the bio-supercomputer looks a bit like a road map of a busy and very organised city as seen from a plane. The chip measures about 1.5cm square in which channels have been etched.

Instead of the electrons that are propelled by an electrical charge and move around within a traditional microchip, short strings of proteins called biological agents travel around the circuit in a controlled way, with their movements powered by ATP.

Because it is run by biological agents, and as a result hardly heats up at all, the model bio-supercomputer uses far less energy than standard electronic supercomputers do, making it more sustainable, researchers said.

Traditional supercomputers use so much electricity, that they heat up a lot and then need to be cooled down, often requiring their own power plant to function.

Although the model bio supercomputer was able to very efficiently tackle a complex classical mathematical problem by using parallel computing of the kind used by supercomputers, the researchers said there is still a lot of work ahead to move from the model they have created to a full-scale functional computer.

The study was published in the journal PNAS.