Roadmap for network-based biocomputation

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

  • Falco C.M.J.M. Van Delft - , Molecular Sense Ltd (Autor:in)
  • Alf Månsson - , Linnaeus University, Lund University (Autor:in)
  • Hillel Kugler - , Bar-Ilan University (Autor:in)
  • Till Korten - , Professur für BioNano-Werkzeuge (Autor:in)
  • Cordula Reuther - , Professur für BioNano-Werkzeuge (Autor:in)
  • Jingyuan Zhu - , Lund University (Autor:in)
  • Roman Lyttleton - , Lund University (Autor:in)
  • Thomas Blaudeck - , Technische Universität Chemnitz, Fraunhofer Institute for Electronic Nano Systems (Autor:in)
  • Christoph Robert Meinecke - , Technische Universität Chemnitz, Fraunhofer Institute for Electronic Nano Systems (Autor:in)
  • Danny Reuter - , Technische Universität Chemnitz, Fraunhofer Institute for Electronic Nano Systems (Autor:in)
  • Stefan Diez - , Professur für BioNano-Werkzeuge, Exzellenzcluster PoL: Physik des Lebens, Max Planck Institute of Molecular Cell Biology and Genetics (Autor:in)
  • Heiner Linke - , Lund University (Autor:in)

Abstract

Network-based biocomputation (NBC) is an alternative, parallel computation approach that can potentially solve technologically important, combinatorial problems with much lower energy consumption than electronic processors. In NBC, a combinatorial problem is encoded into a physical, nanofabricated network. The problem is solved by biological agents (such as cytoskeletal filaments driven by molecular motors) that explore all possible pathways through the network in a massively parallel and highly energy-efficient manner. Whereas there is currently a rapid development in the size and types of problems that can be solved by NBC in proof-of-principle experiments, significant challenges still need to be overcome before NBC can be scaled up to fill a technological niche and reach an industrial level of manufacturing. Here, we provide a roadmap that identifies key scientific and technological needs. Specifically, we identify technology benchmarks that need to be reached or overcome, as well as possible solutions for how to achieve this. These include methods for large-scale production of nanoscale physical networks, for dynamically changing pathways in these networks, for encoding information onto biological agents, for single-molecule readout technology, as well as the integration of each of these approaches in large-scale production. We also introduce figures of merit that help analyze the scalability of various types of NBC networks and we use these to evaluate scenarios for major technological impact of NBC. A major milestone for NBC will be to increase parallelization to a point where the technology is able to outperform the current run time of electronic processors. If this can be achieved, NBC would offer a drastic advantage in terms of orders of magnitude lower energy consumption. In addition, the fundamentally different architecture of NBC compared to conventional electronic computers may make it more advantageous to use NBC to solve certain types of problems and instances that are easy to parallelize. To achieve these objectives, the purpose of this roadmap is to identify pre-competitive research domains, enabling cooperation between industry, institutes, and universities for sharing research and development efforts and reducing development cost and time.

Details

OriginalspracheEnglisch
Aufsatznummer032002
FachzeitschriftNano futures
Jahrgang6
Ausgabenummer3
PublikationsstatusVeröffentlicht - 1 Sept. 2022
Peer-Review-StatusJa

Externe IDs

WOS 000837838900001
ORCID /0000-0002-0750-8515/work/142235551

Schlagworte

Forschungsprofillinien der TU Dresden

Ziele für nachhaltige Entwicklung

Schlagwörter

  • Bacteria, Biocomputation, Cytoskeletal filaments, Molecular motors, Network-based biocomputation, Non-deterministic polynomial (np)-complete problems, Parallel computation, parallel computation, bacteria, cytoskeletal filaments, non-deterministic polynomial (NP)-complete problems, biocomputation, molecular motors, network-based biocomputation