Seeing tissue as a ‘phase of matter’: exploring statistical mechanics for the cell
Bibliographic record
Abstract
The field of tissue engineering aims to produce living, biological constructs which possess the appropriate spatial ordering of cells and their extra cellular matrix products. The complexity of a single cell and its interactions in a large collective have made development of useful models to assist in tissue culture difficult, and consequentially most tissue culture endeavors are limited to trial and error approaches. Some cell types display a natural tendency to spontaneously self-assemble into large domains of parallel-oriented cells. In this work, we show that these cell culture systems can be studied in the context of continuous disorder-order phase transformations. We suggest that collective ordering of the cells is controlled by the amount of noise in the walk of the individual cells (directional persistence) because undifferentiated mesenchymal stem cells display a seven-times higher directional persistence than mature fibroblasts and have a 24-times larger final-oriented domain size, an observation that corresponds with collective ordering in self-propelled particle systems. The study of cell culture systems using analogies derived from statistical mechanics yields simple, practical models offering insight into how a long-range order can be obtained in tissue-engineered constructs, providing a new paradigm for managing operations with large collectives of living cells.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".