Nanolithographic Fabrication Technologies for Network-Based Biocomputation Devices

Abstract

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.

Structural Optimization Design of Microfluidics Chips Based on Fast Sequence Pair Algorithm

“…Based on a Monte Carlo iterative solution strategy, the simulated annealing algorithm is a stochastic optimization algorithm commonly employed for combinatorial optimization problems [31,32]. Drawing inspiration from the annealing process of solid materials in physics, this algorithm provides an effective approximate solution for issues with nondeterministic polynomial (NP) complexity [33][34][35]. The traditional sequence pair algorithm is a classical layout representation method in the field of electronic design automation.…”

Structural Optimization Design of Microfluidic Chips Based on Fast Sequence Pair Algorithm

“…Based on a Monte Carlo iterative solution strategy, the simulated annealing algorithm is a stochastic optimization algorithm commonly employed for combinatorial optimization problems [ 31 , 32 ]. Drawing inspiration from the annealing process of solid materials in physics, this algorithm provides an effective approximate solution for issues with non-deterministic polynomial (NP) complexity [ 33 , 34 , 35 ]. The traditional sequence pair algorithm is a classical layout representation method in the field of electronic design automation.…”