Modeling the effect of elasticity in motile cells
Bibliographic record
Abstract
Cells of living organisms interact in a variety of ways such as through biochemical and mechanical cues.Cancer metastasis, wound healing and embryonic development are all examples of multi-cellular processes whereby collective behavior of cells is understood to emerge, in part, due to the physical interactions between individual cells.The experimental study of such interactions, both in vivo and in vitro are invaluable.Nonetheless, it is often not possible to independently vary specific physical properties of cells without altering other aspects of their behavior.In this thesis we develop two methods to numerically simulate interacting elastic motile 2D cells that explicitly track each individual cell.Both methods allow for large deformations which we show are needed to reproduce observed behavior.The first approach is a phase-field model that has since gained popularity.Yet, large scale simulations are computationally taxing.The second method is a sharp-interface limit of the former that offers a 200 fold speedup.This allows us to fully characterize the behavior across varying parameters and scenarios.In the context of cancer, we show that a soft cell surrounded by stiffer cells leads to rare speed "bursts" where collective behavior drives the cell from a deformed state to a more relaxed state with large velocity, and these bursts lead to higher cell motility.More generally, we demonstrate a 2D solid-liquid transition by varying the cell active velocity and obtain new results near the transition for elasticity mixing, diffusion and cell shape.We conclude by demonstrating the model could also be used to study jamming, cell clusters and adhesion.I would first and foremost like to acknowledge my thesis supervisor, Prof. Martin Grant.None of this work would be possible without his direction, expertise, advise, patience, imperturbable nature, and humor.He gave me the opportunity to explore problems that interested me
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 teacher head, 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".