The Bio4Comp project has the ambitious goal of building a computer with greater processing speed and lower energy consumption than any of the most advanced computers existing today. Ultimately, this could translate into enabling large, error-free security software to be fast enough for practical use, potentially wiping out all current security concerns.

A total of €6.1M have been awarded to an European team of researchers from TU Dresden, Fraunhofer-Gesellschaft, Lund University, Linnaeus University and Bar Ilan University, as well as the British company Molecular Sense.

“Practically all really interesting mathematical problems of our time cannot be computed efficiently with our current computer technology,” says Dan V. Nicolau from Molecular Sense, who had the original idea of harnessing the power of biomolecules to build better computers. The team plans to solve this problem by scaling up its first biocomputer prototype, whose mechanisms have been published in the journal PNAS.

PNAS – Parallel computation with molecular-motor-propelled agents in nanofabricated networks

**Significance**

Electronic computers are extremely powerful at performing a high number of operations at very high speeds, sequentially. However, they struggle with combinatorial tasks that can be solved faster if many operations are performed in parallel. Here, we present proof-of-concept of a parallel computer by solving the specific instance {2, 5, 9} of a classical nondeterministic-polynomial-time complete (“NP-complete”) problem, the subset sum problem. The computer consists of a specifically designed, nanostructured network explored by a large number of molecular-motor-driven, protein filaments. This system is highly energy efficient, thus avoiding the heating issues limiting electronic computers. We discuss the technical advances necessary to solve larger combinatorial problems than existing computation devices, potentially leading to a new way to tackle difficult mathematical problems.

**Abstract**

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.

developed a parallel-computation approach based on encoding combinatorial problems into the geometry of physical networks. We showed that these networks can be manufactured lithographically and explored using independent agents. Using such a device, we demonstrated the solution of one particular three-variable instance of the SSP.

Notably, once the device is loaded with the required number of agents, the effective computational time for NP-complete problems grows only polynomially, e.g., as N2 if the elements of S are approximately equidistantly spaced. This is in contrast to traditional, sequentially operating, electronic computers, where the time required to explore every possible solution sequentially would scale exponentially as 2N. However, it is inherent to combinatorial and NP-complete problems (assuming P! = NP) that the exploration of the entire solution space requires the use of exponentially increasing amounts of some resource, such as time, space, or material. In the present case this fundamental requirement manifests itself in the number of agents needed, which grows exponentially with 2N. Effectively we are trading the need of time for the need of molecular mass.

The error rates of this first device are too large for scaling up to problems containing more than ∼10 variables (see SI Appendix, section S1 for a more detailed scaling analysis).

Nevertheless, we argue that our approach has the potential to be more scalable in practice than other approaches because it offers several advantages: (i) Myosin II and kinesin-1 molecular motors use a distributed energy supply (ATP in the surrounding solution), thus eliminating the need for external forces (such as pressure or an electric potential) to drive the computation. This need inherently prevents, for example, microfluidic approaches from scaling up, because in these devices the pressures needed to pump fluid through the network become prohibitively large for large N (30). (ii) The molecular motors operate in a highly energy-efficient manner. As a result, the approach demonstrated here consumes orders of magnitude less energy per operation compared with both electronic and microfluidic computers, eliminating issues related to the dissipation of heat. Specifically, we estimate an energy cost of 2–5 × 10^−14 J per operation for a molecular-motor-based device compared with about 3–6 × 10^−10 J per operation for the most advanced electronic computers, or an estimated minimum of 10^−12 J per operation for microfluidics-based computers (SI Appendix, section S7). (iii) The networks in which the problems are encoded in our approach are planar and comprise standardized modules, therefore being fully scalable with existing technology

Fabrication of Computational Networks for Use with the Actin–Myosin System. Electron-beam lithography (EBL) was used for pattern formation in a poly(methyl methacrylate) (PMMA) resist on a SiO2-coated Si substrate. After development and O2-plasma-ashing [to ensure that the PMMA was hydrophilic and therefore unable to support motility (27)], the sample was silanized with trimethylchlorosilane to promote motility on the floor of the exposed SiO2 substrate. Wetting of the surface was performed to reduce the possibility of air bubbles forming in the channels.

Fabrication of Computational Networks for Use with the Microtubule–Kinesin System. A silicon wafer was sputter-deposited with Au, sandwiched between two Ti adhesion layers. Next, a quartz layer was deposited, followed by a TiW layer and a ZEP520 positive-tone electron-beam resist layer. After exposure in an EBL system and development, the TiW, the quartz, and the upper Ti layers were etched by reactive ion etching down to the Au layer. Finally, the resist residue and the TiW were removed.

Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.

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