Large-scale-integration and collective oscillations of 2D artificial cells
Bibliographic record
Abstract
The on-chip large-scale-integration of genetically programmed artificial cells capable of exhibiting collective expression patterns is important for fundamental research and biotechnology. Here, we report a 3D biochip with a 2D layout of 1024 DNA compartments as artificial cells on a 5 × 5 mm2 area. Homeostatic cell-free protein synthesis reactions driven by genetic circuits occur inside the compartments. We create a reaction-diffusion system with a 30 × 30 square lattice of artificial cells interconnected by thin capillaries for diffusion of products. We program the connected lattice with a synthetic genetic oscillator and observe collective oscillations. The microscopic dimensions of the unit cell and capillaries set the effective diffusion and coupling strength in the lattice, which in turn affects the macroscopic synchronization dynamics. Strongly coupled oscillators exhibit fast and continuous 2D fronts emanating from the boundaries, which generate smooth and large-scale correlated spatial variations of the oscillator phases. This opens a class of 2D genetically programmed nonequilibrium synthetic multicellular systems, where chemical energy dissipated in protein synthesis leads to large-scale spatiotemporal patterns. Large-scale-integration of artificial cells on a chip can reveal emergent multicellular phenomena. Here, authors report 2D coupled lattices of artificial cells programmed by genetic oscillators, exhibiting synchrony and front propagation with microscopic dimensions affecting collective behaviour.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".