Bimodality in E. coli gene expression: Sources and robustness to genome-wide stresses
Bibliographic record
Abstract
Bacteria evolved genes whose single-cell distributions of expression levels are broad, or even bimodal. Evidence suggests that they might enhance phenotypic diversity for coping with fluctuating environments. We identified seven genes in E. coli with bimodal (low and high) single-cell expression levels under standard growth conditions and studied how their dynamics are modified by environmental and antibiotic stresses known to target gene expression. We found that all genes lose bimodality under some, but not under all, stresses. Also, bimodality can reemerge upon cells returning to standard conditions, which suggests that the genes can switch often between high and low expression rates. As such, these genes could become valuable components of future multi-stable synthetic circuits. Next, we proposed models of bimodal transcription dynamics with realistic parameter values, able to mimic the outcome of the perturbations studied. We explored several models' tunability and boundaries of parameter values, beyond which it shifts to unimodal dynamics. From the model results, we predict that bimodality is robust, and yet tunable, not only by RNA and protein degradation rates, but also by the fraction of time that promoters remain unavailable for new transcription events. Finally, we show evidence that, although the empirical expression levels are influenced by many factors, the bimodality emerges during transcription initiation, at the promoter regions and, thus, may be evolvable and adaptable.
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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 | 0.002 |
| 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 | 0.000 |
| 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 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".