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Record W128333381

Parallelism, Program Size, Time, and Temperature in Self-Assembly

2010· article· en· W128333381 on OpenAlexaboutno aff
Ho-Lin Chen, David Doty, Shinnosuke Seki, David Soloveichik

Bibliographic record

VenuearXiv (Cornell University) · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDNA and Biological Computing
Canadian institutionsnot available
Fundersnot available
KeywordsTileSquare (algebra)Upper and lower boundsCombinatoricsParallelism (grammar)Binary logarithmComputer scienceOrder (exchange)Parallel computingDiscrete mathematicsMathematicsGeometryMaterials science
DOInot available

Abstract

fetched live from OpenAlex

We settle a number of questions in variants of Winfree’s abstract Tile Assembly Model (aTAM), a model of molecular algorithmic self-assembly. In the “hierarchical” aTAM, two assemblies, both consisting of multiple tiles, are allowed to aggregate together, whereas in the “seeded” aTAM, tiles attach one at a time to a growing assembly. Adleman, Cheng, Goel, and Huang (Running Time and Program Size for Self-Assembled Squares, STOC 2001) showed how to assemble an n × n square in O(n) time in the seeded aTAM using O( logn log logn ) unique tile types, showed that both of these parameters are optimal, and asked whether the hierarchical aTAM could allow a tile system to use the ability to form large assemblies in parallel before they attach to break the Ω(n) lower bound for assembly time. We show there is a tile system with the optimal O( log n log logn ) tile types that assembles an n × n square using O(log n) parallel “stages”, which is close to the optimal Ω(log n) stages, forming the final n×n from four n/2×n/2 squares, which are themselves recursively formed from n/4×n/4 squares, etc. However, despite this nearly maximal parallelism, the system requires superlinear time to assemble the square. We leave open the question of whether some hierarchical tile system can break the Ω(n) assembly time lower bound for assembling an n× n square. We extend the definition of partial order tile systems studied by Adleman et al. in a natural way to hierarchical assembly and show that no hierarchical partial order tile system can build any shape with diameter N in less than time Ω(N), demonstrating that in this case the hierarchical model affords no speedup whatsoever over the seeded model. We also strengthen the Ω(N) time lower bound for deterministic seeded systems of Adleman et al. to nondeterministic seeded systems. We then investigate the relationship between the temperature of a tile system and its size. We show that a tile system can in general require temperature that is exponentially greater than its number of tile types. On the other hand, for the special case of 2-cooperative systems, in which all binding events involve at most 2 sides of tiles, it suffices to use temperature linear in the number of tile types. We show that there is a polynomial-time algorithm that, given any tile system T specified by its desired binding behavior, finds a temperature and binding energies (at most exponential in the number of tile types of T ) that realize this behavior or reports that no such energies exist. This result is applied to show that there is a polynomial-time algorithm that, given an n× n square Sn, determines the smallest (non-hierarchical “seeded”) system (at any temperature) that is deterministic and self-assembles Sn. This answers an open question of Adleman, Cheng, Goel, Huang, Kempe, Moisset de Espanes, and Rothemund (Combinatorial Optimization Problems in Self-Assembly, STOC 2002). The first author was supported by the Molecular Programming Project under NSF grant 0832824, the second and fourth authors were supported by NSF Computing Innovation Fellowships, and the third author was supported by NSERC Discovery Grant R2824A01 and the Canada Research Chair in Biocomputing to Lila Kari. California Institute of Technology, Pasadena, CA, USA, holinc@gmail.com, ddoty@caltech.edu University of Western Ontario, Dept. of Computer Science, London, ON, Canada, N6A 5B7, sseki@csd.uwo.ca. University of Washington, Dept. of Computer Science, Seattle, WA, USA, dsolov@u.washington.edu.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0020.009
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.173
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2010
Admission routes1
Has abstractyes

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