Error-rate reduction in network-based biocomputation
Pradheebha Surendiran, Marko U\v{s}aj, Till Korten, Alf M{\aa}nsson,, Heiner Linke

TL;DR
This paper optimizes the geometry of pass junctions in network-based biocomputation to significantly reduce error rates, enabling larger and more scalable molecular computing networks with potential for further improvements using 3D structures.
Contribution
It introduces a systematic approach to design and experimentally validate low-error pass junctions in NBC, achieving under 1% error rate and highlighting the need for 3D junctions for further reduction.
Findings
Optimized pass junction design reduced error rate to below 1%.
Experimental and simulation results aligned better with a layer of myosin included.
Further error reduction likely requires three-dimensional junctions.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCell Image Analysis Techniques · Neural Networks and Applications · Evolutionary Algorithms and Applications
