Design and fabrication of networks for bacterial computing
Bibliographic record
Abstract
Abstract Non-deterministic polynomial (NP-) complete problems, whose number of possible solutions grows exponentially with the number of variables, require by necessity massively parallel computation. Because sequential computers, such as solid state-based ones, can solve only small instances of these problems within a reasonable time frame, parallel computation using motile biological agents in nano- and micro-scale networks has been proposed as an alternative computational paradigm. Previous work demonstrated that protein molecular motors-driven cytoskeletal filaments are able to solve a small instance of an NP complete problem, i.e. the subset sum problem, embedded in a network. Autonomously moving bacteria are interesting alternatives to these motor driven filaments for solving such problems, because they are easier to operate with, and have the possible advantage of biological cell division. Before scaling up to large computational networks, bacterial motility behaviour in various geometrical structures has to be characterised, the stochastic traffic splitting in the junctions of computation devices has to be optimized, and the computational error rates have to be minimized. In this work, test structures and junctions have been designed, fabricated, tested, and optimized, leading to specific design rules and fabrication flowcharts, resulting in correctly functioning bio-computation networks.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".