Division Asymmetry Drives Cell Size Variability in Budding Yeast
Bibliographic record
Abstract
Abstract Cell size variability within proliferating populations reflects the interdependent regulation of cell growth and division as well as intrinsically stochastic effects. In budding yeast, the G1/S transition exerts strong size control in daughter cells, which manifests as the inverse correlation between how big a cell is when it is born and how much it grows in G1. However, mutations affecting this size control checkpoint only modestly influence population-wide size variability, often altering the coefficient of variation (CV) only by ∼10%. To resolve this paradox, we combine computational modeling and live-cell imaging to identify the principal determinants of cell size variability. Using an experimentally validated stochastic model of the yeast cell cycle, we perform parameter sensitivity analysis and find that division asymmetry between mothers and daughters is the dominant driver of CV, outweighing the effects of G1/S size control. Experimental measurements across genetic perturbations and growth conditions confirm a strong correlation between mother-daughter size asymmetry and population CV. These findings reconcile previous observations and show how asymmetric division operates in concert with G1/S size control to govern cell size heterogeneity.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".