Significant advancements have been made towards exploitation of naturally available molecular motors and their associated cytoskeletal filaments in nanotechnological applications. For instance, myosin motors and actin filaments from muscle have been used with the aims to establish new approaches in biosensing and network-based biocomputation. The basis for these developments is a version of the in vitro motility assay (IVMA) where surface-adsorbed myosin motors propel the actin filaments along suitably derivatized nano-scale channels on nanostructured chips. These chips are generally assembled into custom-made microfluidic flow cells. For effective applications, particularly in biocomputation, it is important to appreciably prolong function of the biological system. Here, we systematically investigated potentially critical factors necessary to achieve this, such as biocompatibility of different components of the flow cell, the degree of air exposure, assay solution composition and nanofabrication methods. After optimizing these factors we prolonged the function of actin and myosin in nanodevices for biocomputation from <20 min to >60 min. In addition, we demonstrated that further optimizations could increase motility run times to >20 h. Of great importance for the latter development was a switch of glucose oxidase in the chemical oxygen scavenger system (glucose oxidase–glucose–catalase) to pyranose oxidase, combined with the use of blocking actin (non-fluorescent filaments that block dead motors). To allow effective testing of these approaches we adapted commercially available microfluidic channel slides, for the first time demonstrating their usefulness in the IVMA. As part of our study, we also demonstrate that myosin motor fragments can be stored at −80 °C for more than 10 years before use for nanotechnological purposes. This extended shelf-life is important for the sustainability of network-based biocomputation.

Molecular motor-driven filament systems have been extensively explored for biomedical and nanotechnological applications such as lab-on-chip molecular detection or network-based biocomputation. In these applications, filament transport conventionally occurs in two dimensions (2D), often guided along open, topographically and/or chemically structured channels which are coated by molecular motors. However, at crossing points of different channels the filament direction is less well determined and, though crucial to many applications, reliable guiding across the junction can often not be guaranteed. We here present a three-dimensional (3D) approach that eliminates the possibility for filaments to take wrong turns at junctions by spatially separating the channels crossing each other. Specifically, 3D junctions with tunnels and overpasses were manufactured on glass substrates by two-photon polymerization, a 3D fabrication technology where a tightly focused, femtosecond-pulsed laser is scanned in a layer-to-layer fashion across a photo-polymerizable inorganic–organic hybrid polymer (ORMOCER®) with µm resolution. Solidification of the polymer was confined to the focal volume, enabling the manufacturing of arbitrary 3D microstructures according to computer-aided design data. Successful realization of the 3D junction design was verified by optical and electron microscopy. Most importantly, we demonstrated the reliable transport of filaments, namely microtubules propelled by kinesin-1 motors, across these 3D junctions without junction errors. Our results open up new possibilities for 3D functional elements in biomolecular transport systems, in particular their implementation in biocomputational networks.

Inspired by molecular motors in biology, there has been significant progress in building artificial molecular motors, using a number of quite distinct approaches. As the constructs become more sophisticated, there is also an increasing need to directly observe the motion of artificial motors at the nanoscale and to characterize their performance. Here, we review the most used methods that tackle those tasks. We aim to help experimentalists with an overview of the available tools used for different types of synthetic motors and to choose the method most suited for the size of a motor and the desired measurements, such as the generated force or distances in the moving system. Furthermore, for many envisioned applications of synthetic motors, it will be a requirement to guide and control directed motions. We therefore also provide a perspective on how motors can be observed on structures that allow for directional guidance, such as nanowires and microchannels. Thus, this Review facilitates the future research on synthetic molecular motors, where observations at a single-motor level and a detailed characterization of motion will promote applications.

The 3‐satisfiability Problem (3‐SAT) is a demanding combinatorial problem that is of central importance among the nondeterministic polynomial (NP) complete problems, with applications in circuit design, artificial intelligence, and logistics. Even with optimized algorithms, the solution space that needs to be explored grows exponentially with the increasing size of 3‐SAT instances. Thus, large 3‐SAT instances require excessive amounts of energy to solve with serial electronic computers. Network‐based biocomputation (NBC) is a parallel computation approach with drastically reduced energy consumption. NBC uses biomolecular motors to propel cytoskeletal filaments through nanofabricated networks that encode mathematical problems. By stochastically exploring possible paths through the networks, the cytoskeletal filaments find possible solutions. However, to date, no NBC algorithm for 3‐SAT has been available. Herein, an algorithm that converts 3‐SAT into an NBC‐compatible network format is reported and four small 3‐SAT instances (with up to three variables and five clauses) using the actin–myosin biomolecular motor system are experimentally solved. Because practical polynomial conversions to 3‐SAT exist for many important NP complete problems, the result opens the door to enable NBC to solve small instances of a wide range of problems.

Information processing by traditional, serial electronic processors consumes an ever-increasing part of the global electricity supply. An alternative, highly energy efficient, parallel computing paradigm is network-based biocomputation (NBC). In NBC a given combinatorial problem is encoded into a nanofabricated, modular network. Parallel exploration of the network by a very large number of independent molecular-motor-propelled protein filaments solves the encoded problem. Here we demonstrate a significant scale-up of this technology by solving four instances of Exact Cover, a nondeterministic polynomial time (NP) complete problem with applications in resource scheduling. The difficulty of the largest instances solved here is 128 times greater in comparison to the current state of the art for NBC.