Evidence map›Paper›PMID 26903637›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2016

Parallel computation with molecular-motor-propelled agents in nanofabricated networks.

Dan V Nicolau, Mercy Lard, Till Korten, Falco C M J M van Delft, Malin Persson, Elina Bengtsson, Alf Månsson, Stefan Diez, Heiner Linke, Dan V Nicolau

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.

0numbers the graph read from it
0cells of the map it votes in
46citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

46 citing papers in PubMed.

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  15. Electro-detachment of kinesin motor domain from microtubule in silico.Computational and structural biotechnology journal · 2023
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Dan V NicolauDepartment of Integrative Biology, University of California, Berkeley, CA 94720-3140; Molecular Sense, Ltd., Wallasey CH44 1AJ, United Kingdom;
Mercy LardNanoLund and Solid State Physics, Lund University, S-22100 Lund, Sweden;
Till KortenCenter for Molecular Bioengineering (B CUBE) and Center for Advancing Electronics Dresden (cfaed), Technische Universität Dresden, 01069 Dresden, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
Falco C M J M van DelftPhilips Research (MiPlaza) and Philips Innovation Services, 5656 AE, Eindhoven, The Netherlands;
Malin PerssonDepartment of Chemistry and Biomedical Sciences, Linnaeus University, SE-39182 Kalmar, Sweden;
Elina BengtssonDepartment of Chemistry and Biomedical Sciences, Linnaeus University, SE-39182 Kalmar, Sweden;
Alf MånssonDepartment of Chemistry and Biomedical Sciences, Linnaeus University, SE-39182 Kalmar, Sweden;
Stefan DiezCenter for Molecular Bioengineering (B CUBE) and Center for Advancing Electronics Dresden (cfaed), Technische Universität Dresden, 01069 Dresden, Germany; Max Planck Institute of Molecular Cell Biology and Genetics, 01307 Dresden, Germany;
Heiner LinkeNanoLund and Solid State Physics, Lund University, S-22100 Lund, Sweden; heiner.linke@ftf.lth.se dan.nicolau@mcgill.ca.
Dan V NicolauDepartment of Electrical Engineering & Electronics, University of Liverpool, Liverpool L69 3GJ, United Kingdom; Department of Bioengineering, McGill University, Montreal, QC, Canada H3A 0C3 heiner.linke@ftf.lth.se dan.nicolau@mcgill.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The combinatorial nature of many important mathematical problems, including nondeterministic-polynomial-time (NP)-complete problems, places a severe limitation on the problem size that can be solved with conventional, sequentially operating electronic computers. There have been significant efforts in conceiving parallel-computation approaches in the past, for example: DNA computation, quantum computation, and microfluidics-based computation. However, these approaches have not proven, so far, to be scalable and practical from a fabrication and operational perspective. Here, we report the foundations of an alternative parallel-computation system in which a given combinatorial problem is encoded into a graphical, modular network that is embedded in a nanofabricated planar device. Exploring the network in a parallel fashion using a large number of independent, molecular-motor-propelled agents then solves the mathematical problem. This approach uses orders of magnitude less energy than conventional computers, thus addressing issues related to power consumption and heat dissipation. We provide a proof-of-concept demonstration of such a device by solving, in a parallel fashion, the small instance {2, 5, 9} of the subset sum problem, which is a benchmark NP-complete problem. Finally, we discuss the technical advances necessary to make our system scalable with presently available technology.

Indexed as

biocomputationmolecular motorsnanotechnologyNP completeparallel computing

Identifiers

PMID26903637
PMCPMC4791004

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.