Generation of a stochastic, agent-based mathematical model to predict the memory CD8+ T cell response to vaccination 3677
Bibliographic record
Abstract
Abstract Description Protective immunity against infection requires both pathogen-specific antibody and T cell responses. Current vaccine strategies rely on the generation of protective antibody responses, but these vaccines fail to yield the memory CD8+ T cell populations required for protection against several pathogens including the intracellular parasites that cause malaria and toxoplasmosis. We show that a single low dose immunization with an attenuated strain of Toxoplasma gondii generates a protective, pathogen-specific CD8+ T cell response. To understand the mechanisms underlying this response, a mathematical, stochastic, agent-based model was developed to track every CD8+ T cell and simulate the events from T cell priming and expansion to T cell differentiation, contraction, and memory formation across secondary lymphoid tissues and the site of immunization. Early studies with the model accurately predicted biological defects in the CD8+ T cell response in cDC1-deficient mice, demonstrating the predictive capacity of the model. Importantly, the stochasticity of the model allows for a more physiologically accurate recapitulation than previous deterministic models of the variation in CD8+ T cell responses observed in mice as well as in human studies following vaccination. Going forward, the model will be applied to mRNA-LNP vaccination to inform their biology and streamline their development toward a strategy that effectively produces a protective CD8+ T cell response. Funding Sources Supported by NIH AI-160664 Topic Categories Computational and Systems Immunology (COMP)
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".