Introducing Cell Cycle Regulation to a Mathematical Model of the T-cell Proliferative Phase
Bibliographic record
Abstract
CD8+ T cells are critical to the adaptive immune response and are a target for vaccine development. However, the complex dynamics of cell proliferation can vary response success, providing uncertainty when designing vaccines. Computer models can provide clarity by simulating these dynamics, tracking millions of cell-cell interactions, a feat that is impractical experimentally. Our group created the STORE.1 model, a probabilistic simulation of the CD8+ T cell response to vaccination. While able to accurately simulate in vivo mouse T cell clonal expansion, intracellular dynamics are absent. Furthermore, there is no mechanism by which cell division ceases. This work builds upon the STORE.1 model by systematically explaining the division dynamics of CD8+ T cells and providing measures of the extracellular environment. The new STORE.2 model has demonstrated an ability to accurately simulate differences in CD8+ T cell expansion in WT mice and mice lacking type I conventional dendritic cells up to 170 hours after vaccination. It is the first model to simulate individual cell cycle regulator protein counts for millions of cells, and the resulting impact on pH for the extracellular microenvironment. Finally, it provides a partial mechanism behind division cessation, an important element for future models seeking to further simulate the end of the T-cell response.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".