Biological supercomputers to be powered by molecular motors

Today’s computers use vast amounts of electric power – so much so that the inability to cool the processors actually hampers the development of more powerful computers. In addition, they cannot do two things at the same time, which affects the processing speed needed.

The EU is now funding a large project that aims to develop technology for an extremely powerful computer based on highly efficient molecular motors. The motors will use a fraction of the energy of existing computers, and will be able to tackle problems where many solutions need to be explored simultaneously.

"One of the most exciting aspects of network-based computing with molecular motors is that it needs hundred to thousand times less energy than electronic computers.", says Professor Heiner Linke, Director of the NanoLund Center for Nanoscience at Lund University and coordinator of the project.

“Practically all really interesting mathematical problems of our time cannot be computed efficiently with our current computer technology.” adds Dan V. Nicolau, Ph.D. M.D., from the UK-based enterprise Molecular Sense, who had the original idea of using biomolecular motors as computers.

The idea is that biomolecular machines, each only a few billionth of a meter (nanometers) in size, can solve problems by moving through a nanofabricated network of channels designed to represent a mathematical algorithm - an approach they call “network-based biocomputation”.

Whenever the biomolecules reach a junction in the network, they can decide to add a number to the sum they are calculating or leave it out. That way, each of the myriad of biomolecules acts as a tiny computer with processor and memory.

While an individual biomolecule is much slower than a current computer, they are self-assembling so that they can be used in large numbers, quickly adding up their computing power. The researchers have demonstrated that this works in a publication in the prestigious scientific periodical PNAS.

”The biological computing units can multiply themselves to adapt to the difficulty of the mathematical problem”, explains Till Korten, Ph.D. from TU Dresden, co-coordinator of the project.