Computational modeling of cell sorting, tissue engulfment, and related phenomena: A review
Bibliographic record
Abstract
Embryonic cells have the remarkable ability to spontaneously reposition themselves with respect to other cells in an aggregate, an ability that is central to embryo morphogenesis, many disease processes, wound healing, and tissue engineering. In these rearrangements, cells of two or more histological types in a heterotypic aggregate can sort, mix or form checkerboard patterns and contacting fragments of different homogeneous tissues can spread over or engulf one another. In this article, the experimental literature on cell and tissue reorganization is summarized, the main sub-cellular structural components are identified and hypotheses about how these components interact to drive specific patterns of rearrangement are outlined. Making extensive use of tables, the article then maps out the interplay between experiments, theories, ultrastructural discoveries and computer models in the advancement of the field. The article summarizes the main computational approaches, including cell and sub-cellular lattices, body centric, boundary vertex and finite element models. The principle of operation, advantages and disadvantages of each approach is discussed, and the contributions of representative papers noted. Strong commonalities are found in the physical basis of the models and in the predictions they make. Computational models provide an important ongoing complement to experimental and theoretical studies. This review article cites 154 references.
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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".