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In the in vitro motility assay (IVMA), actin filaments are observed while propelled by surface-adsorbed myosin motor fragments such as heavy meromyosin (HMM). In addition to fundamental studies, the IVMA is the basis for a range of lab-on-a-chip applications, e.g. transport of cargoes in nanofabricated channels in nanoseparation/biosensing or the solution of combinatorial mathematical problems in network-based biocomputation. In these applications, prolonged myosin function is critical as is the potential to repeatedly exchange experimental solutions without functional deterioration. We here elucidate key factors of importance in these regards. Our findings support a hypothesis that early deterioration in the IVMA is primarily due to oxygen entrance into in vitro motility assay flow cells. In the presence of a typically used oxygen scavenger mixture (glucose oxidase, glucose, and catalase), this leads to pH reduction by a glucose oxidase-catalyzed reaction between glucose and oxygen but also contributes to functional deterioration by other mechanisms. Our studies further demonstrate challenges associated with evaporation and loss of actin filaments with time. However, over 8 h at 21–26 °C, there is no significant surface desorption or denaturation of HMM if solutions are exchanged manually every 30 min. We arrive at an optimized protocol with repeated exchange of carefully degassed assay solution of 45 mM ionic strength, at 30 min intervals. This is sufficient to maintain the high-quality function in an IVMA over 8 h at 21–26 °C, provided that fresh actin filaments are re-supplied in connection with each assay solution exchange. Finally, we demonstrate adaptation to a microfluidic platform and identify challenges that remain to be solved for real lab-on-a-chip applications.

Exact cover is a non-deterministic polynomial time (NP)—complete problem that is central to optimization challenges such as airline fleet planning and allocation of cloud computing resources. Solving exact cover requires the exploration of a solution space that increases exponentially with cardinality. Hence, it is time- and energy consuming to solve large instances of exact cover by serial computers. One approach to address these challenges is to utilize the inherent parallelism and high energy efficiency of biological systems in a network-based biocomputation (NBC) device. NBC is a parallel computing paradigm in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. The network is then explored in parallel using a large number of biological agents, such as molecular-motor-propelled protein filaments. The answer to the combinatorial problem can then be inferred by measuring the positions through which the agents exit the network. Here, we (i) show how exact cover can be encoded and solved in an NBC device, (ii) define a formalization that allows to prove the correctness of our approach and provides a mathematical basis for further studying NBC, and (iii) demonstrate various optimizations that significantly improve the computing performance of NBC. This work lays the ground for fabricating and scaling NBC devices to solve significantly larger combinatorial problems than have been demonstrated so far.

The high energy consumption of electronic data processors, together with physical challenges limiting their further improvement, has triggered intensive interest in alternative computation paradigms. Here we focus on network-based biocomputation (NBC), a massively parallel approach where computational problems are encoded in planar networks implemented with nanoscale channels. These networks are explored by biological agents, such as biological molecular motor systems and bacteria, benefitting from their energy efficiency and availability in large numbers. We analyse and define the fundamental requirements that need to be fulfilled to scale up NBC computers to become a viable technology that can solve large NP-complete problem instances faster or with less energy consumption than electronic computers. Our work can serve as a guide for further efforts to contribute to elements of future NBC devices, and as the theoretical basis for a detailed NBC roadmap.

Network-based biocomputation (NBC) relies on accurate guiding of biological agents through nanofabricated channels produced by lithographic patterning techniques. Here, we report on the large-scale, wafer-level fabrication of optimized microfluidic channel networks (NBC networks) using electron-beam lithography as the central method. To confirm the functionality of these NBC networks, we solve an instance of a classical non-deterministic-polynomial-time complete (“NP-complete”) problem, the subset-sum problem. The propagation of cytoskeletal filaments, e.g., molecular motor-propelled microtubules or actin filaments, relies on a combination of physical and chemical guiding along the channels of an NBC network. Therefore, the nanofabricated channels have to fulfill specific requirements with respect to the biochemical treatment as well as the geometrical confienement, with walls surrounding the floors where functional molecular motors attach. We show how the material stack used for the NBC network can be optimized so that the motor-proteins attach themselves in functional form only to the floor of the channels. Further optimizations in the nanolithographic fabrication processes greatly improve the smoothness of the channel walls and floors, while optimizations in motor-protein expression and purification improve the activity of the motor proteins, and therefore, the motility of the filaments. Together, these optimizations provide us with the opportunity to increase the reliability of our NBC devices. In the future, we expect that these nanolithographic fabrication technologies will enable production of large-scale NBC networks intended to solve substantially larger combinatorial problems that are currently outside the capabilities of conventional software-based solvers.

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. Herein, an algorithm that encodes the 3‐satisfiability problem (3‐SAT) into network format is presented. Four small 3‐SAT instances with up to three variables and five clauses are solved experimentally; using myosin‐propelled actin filaments exploring nanofabricated 3‐SAT networks in a highly energy‐efficient manner.

Over the last 25 years, extensive progress has been made in developing a range of nanotechnological applications where cytoskeletal filaments and molecular motors are key elements. This includes novel, highly miniaturized lab on a chip systems for biosensing, nanoseparation etc but also new materials and parallel computation devices for solving otherwise intractable mathematical problems. For such approaches, both actin-based and microtubule-based cytoskeletal systems have been used. However, in accordance with their different cellular functions, actin filaments and microtubules have different properties and interaction kinetics with molecular motors. Therefore, the two systems obviously exhibit different advantages and encounter different challenges when exploited for applications. Specifically, the achievable filament velocities, the capability to guide filaments along nanopatterned tracks and the capability to attach and transport cargo differ between actin- and microtubule-based systems. Our aim here is to systematically elucidate these differences to facilitate design of new devices and optimize future developments. We first review the cellular functions and the fundamental physical and biochemical properties of actin filaments and microtubules. In this context we also consider their interaction with molecular motors and other regulatory proteins that are of relevance for applications. We then relate these properties to the advantages and challenges associated with the use of each of the motor-filament systems for different tasks. Finally, fundamental properties are considered in relation to some of the most interesting future development paths e.g. in biosensing and biocomputation.