  Network-based biocomputation (NBC) is an alternative parallel computing
paradigm that encodes combinatorial problems into a nanofabricated device's
graphical network of channels, enabling cytoskeletal filaments propelled by
molecular motors to explore the problems' solution space. NBC promises to
require significantly less energy than traditional computers due to the high
energy efficiency of molecular motors. However, error rates associated with the
pass junction crossing, the primary path-regulating geometry, pose a bottleneck
for scaling up this technology. Here, we optimize the geometry of the pass
junction for low error rates for the actin-myosin system. To do so, we evaluate
various pass junction designs that differ in features, such as the nanochannel
width, junction crossing area, and angles of a funnel-shaped output part of the
junction. Error rates were measured experimentally by using gliding motility
assay and as well as by simulation methods. The final optimized design
displayed a decreased error rate of under 1 percent compared to the previous
2-4 percent. We anticipate this improvement will enable scaling up NBC networks
from tens to hundreds of pass junctions. However, the results of 2D junction
optimizations also suggest that further drastic reduction of error rates in
two-dimensional pass junctions is unlikely, necessitating three-dimensional
junctions, such as bridges or tunnels, for complete error rate mitigation.
Furthermore, the simulation results demonstrate that including a layer of
myosin motor on the channel provides a better fit between simulation and
experimental results.
