The STORE.2 model of the T cell proliferative phase considering c-Myc
Bibliographic record
Abstract
ABSTRACT The generation of antigen-specific CD8 + T cell responses is dictated by the affinity of the cognate antigen, the stimulatory capacity of antigen presenting cells (APCs), and the metabolic pathways required for rapid cell proliferation. The complexity of these pathways is a significant challenge in designing vaccines against diseases that require a protective CD8 + T cell response. To understand the mechanisms underlying CD8 + T cell responses, the STORE.2 model was developed to simulate the early events of T cell priming and expansion at the site of priming. STORE.2 is a mathematical, stochastic, and agent-based model based on first-principles that tracks every individual CD8 + T cell and APC. It allows for the simulation of different antigen affinity (Signal 1) as well as levels of costimulation (Signal 2) and inflammatory cytokines (Signal 3) provided by APCs. The impact of Signals 1-3 is translated to T cell responses via the transcription factor c-Myc, which supports T cell proliferation. Enhanced glycolysis during T cell activation results in a change in the metabolic environment that includes an increase in extracellular lactate concentration. STORE.2 models the role of lactate in the inhibition of T cell metabolism and proliferation as a negative feedback mechanism on c-Myc production. STORE.2 accurately recapitulated the CD8 + T cell response during in vitro T cell priming assays that control for Signals 1-3 as well as lactate concentration. Finally, application of STORE.2 to an in vivo response to immunization demonstrated that the model accurately simulates CD8 + T cell activation at the site of priming.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".