Biomolecular motors enables an incredibly efficient biocomputer

02/29/2016 - 07:48

Dan V. Nicolau, Jr.

Researchers at Lund University in Sweden have utilised nanotechnology to create a biological computer that can solve certain mathematical problems far faster and more energy-efficiently than conventional electrical computers. The research results have now been published in the prestigious publication Proceedings of the National Academy of Sciences (PNAS).

Conventional computers have contributed to major advances for society over the past few decades, but have a weakness: they can only do one thing at a time.


Ref: Parallel computation with molecular-motor-propelled agents in nanofabricated networks. Proceedings of the National Academy of Sciences (18 January 2016) | DOI: 10.1073/pnas.1510825113


The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.