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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".