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

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, molecularmotor-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 NPcomplete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.parallel computing | molecular motors | NP complete | biocomputation | nanotechnology M any combinatorial problems of practical importance, such as the design and verification of circuits (1), the folding (2) and design (3) of proteins, and optimal network routing (4), require that a large number of possible candidate solutions are explored in a brute-force manner to discover the actual solution. Because the time required for solving these problems grows exponentially with their size, they are intractable for conventional electronic computers, which operate sequentially, leading to impractical computing times even for medium-sized problems. Solving such problems therefore requires efficient parallel-computation approaches (5). However, the approaches proposed so far suffer from drawbacks that have prevented their implementation. For example, DNA computation, which generates mathematical solutions by recombining DNA strands (6, 7), or DNA static (8) or dynamic (9) nanostructures, is limited by the need for impractically large amounts of DNA (10-13). Quantum computation is limited in scale by decoherence and by the small number of qubits that can be integrated (14). Microfluidics-based parallel computation (15) is difficult to scale up in practice due to rapidly diverging physical size and complexity of the computation devices with the size of the problem, as well as the need for impractically large external pressure.Here, we propose a parallel-computation approach, which is based on encoding combinatorial problems into the geometry of a physical network of lithographically defined channels, followed by exploration of the network in a par...

Actomyosin based contraction: one mechanokinetic model from single molecules to muscle?

“…by illumination (Nakamura et al 2014). In addition to potential uses in nanotechnological applications (Kumar et al 2016; Månsson 2012; Nicolau et al 2016) the engineering results pose new questions about how certain functions relate to structural elements in the motor. The present model, by virtue of coarse-grain structural equivalence of different states, would be useful for elucidating how the engineered elements give rise to some of their functional consequences.…”

“…We also consider possible usefulness in guiding engineering of myosin motors (Amrute-Nayak et al 2010; Nakamura et al 2014; Schindler et al 2014; Tsiavaliaris et al 2004) e.g. for optimized use in nanotechnological applications (Kumar et al 2016; Månsson 2012; Nicolau et al 2016). …”

Non-equilibrium assembly of microtubules: from molecules to autonomous chemical robots

“…Nicolau’s biocomputation concept 160 (Fig. 8c) relies on a large number of kinesin-propelled microtubules (or myosin-propelled actin filaments) to traverse a network of channels encoding a mathematical problem.…”

“…(c) Microtubules traversing a network of channels can be used for energy-efficient biocomputation. From 160 . (d) DNA connections introduce programmability into the self-organization process.…”

Mechanical splitting of microtubules into protofilament bundles by surface-bound kinesin-1

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