Coevolution of Codon Usage and tRNA Genes Leads to Alternative Stable States of Biased Codon Usage
Bibliographic record
Abstract
The typical number of tRNA genes in bacterial genomes is around 50, but this number varies from under 30 to over 120. We argue that tRNA gene copy numbers evolve in response to translational selection. In rapidly multiplying organisms, the time spent in translation is a limiting factor in cell division; hence, it pays to duplicate tRNA genes, thereby increasing the concentration of tRNA molecules in the cell and speeding up translation. In slowly multiplying organisms, translation time is not a limiting factor, so the overall translational cost is minimized by reducing the tRNAs to only one copy of each required gene. Translational selection also causes a preference for codons that are most rapidly translated by the current tRNAs; hence, codon usage and tRNA gene content will coevolve to a state where each is adapted to the other. We show that there is often more than one stable coevolved state. This explains why different combinations of tRNAs and codon bias can exist for different amino acids in the same organism. We analyze a set of 80 complete bacterial genomes and show that the theory predicts many of the trends that are seen in these data.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".