Bibliographic record
Abstract
Abstract While most of our tissues appear static, in fact, cell motion comprises an important facet of all life forms, whether in single or multicellular organisms. Amoeboid cells navigate their environment seeking nutrients, whereas collectively, streams of cells move past and through evolving tissue in the development of complex organisms. Cell motion is powered by dynamic changes in the structural proteins (actin) that make up the cytoskeleton, and regulated by a circuit of signaling proteins (GTPases) that control the cytoskeleton growth, disassembly, and active contraction. Interesting mathematical questions we have explored include (1) How do GTPases spontaneously redistribute inside a cell? How does this determine the emergent polarization and directed motion of a cell? (2) How does feedback between actin and these regulatory proteins create dynamic spatial patterns (such as waves) in the cell? (3) How do properties of single cells scale up to cell populations and multicellular tissues given interactions (adhesive, mechanical) between cells? Here I survey mathematical models studied in my group to address such questions. We use reaction-diffusion systems to model GTPase spatiotemporal phenomena in both detailed and toy models (for analytic clarity). We simulate single and multiple cells to visualize model predictions and study emergent patterns of behavior. Finally, we work with experimental biologists to address data-driven questions about specific cell types and conditions.
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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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".