Plasmid-borne prokaryotic gene expression: Sources of variability and quantitative system characterization
Bibliographic record
Abstract
One aim of synthetic biology is to exert systematic control over cellular behavior, either for medical purposes or to "program" microorganisms. An engineering approach to the design of biological controllers demands a quantitative understanding of the dynamics of both the system to be controlled and the controllers themselves. Here we focus on a widely used method of exerting control in bacterial cells: plasmid vectors bearing gene-promoter pairs. We study two variants of the simplest such element, an unregulated promoter constitutively expressing its gene, against the varying genomic background of four Escherichia coli cell strains. Absolute protein numbers and rates of expression vary with both cell strain and plasmid type, as does the variability of expression across the population. Total variability is most strongly coupled to the cell division process, and after cell size is scaled away, plasmid copy number regulation emerges as a significant effect. We present simple models that capture the main features of the system behavior. Our results confirm that complex interactions between plasmids and their hosts can have significant effects on both expression and variability, even in deliberately simplified systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".