Dynamic Regulation of the Immune Repertoire of Bacteria
Bibliographic record
Abstract
The CRISPR-Cas system provides adaptive immunity in many bacteria and archaea by storing short fragments of viral DNA, known as spacers, in dedicated genomic arrays. A longstanding question in CRISPR-virus coevolution is the optimal number of spacers for each bacterium to maintain proper phage coverage. In this study, we investigate the optimal CRISPR memory size by combining steady-state immune models with dynamical antigenic traveling wave theory to obtain both analytic and numerical results of coevolutionary dynamics. We focus on two experimentally supported phenomena that shape immune dynamics: primed acquisition, where partial spacer-protospacer matches boost acquisition rates, and cassette expansion, where a short-term increase in memory size drives population dynamics. We find that under primed acquisition, longer optimal arrays benefit from maintaining multiple, partially matching spacers. In contrast, dynamic cassette expansion favors shorter arrays by amplifying the fitness advantage of acquiring a few highly effective new spacers. Together, our results highlight that memory optimality is not fixed, but instead shaped by the interaction of acquisition dynamics and population-level immune pressures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".