Modulation of Chemical Composition and Other Parameters of the Cell at Different Exponential Growth Rates
Bibliographic record
Abstract
This review begins by briefly presenting the history of research on the chemical composition and other parameters of cells of E. coli and S. enterica at different exponential growth rates. Studies have allowed us to determine the in vivo strength of promoters and have allowed us to distinguish between factor-dependent transcriptional control of the promoter and changes in promoter activity due to changes in the concentration of free functional RNA polymerase associated with different growth conditions. The total, or bulk, amounts of RNA and protein are linked to the growth rate, because most bacterial RNA is ribosomal RNA (rRNA). Since ribosomes are required for protein synthesis, their number and their rate of function determine the rate of protein synthesis and cytoplasmic mass accumulation. Many mRNAs made in the presence of amino acids have strong ribosome binding sites whose presence reduces the expression of all other active genes. This implies that there can be profound differences in the spectrum of gene activities in cultures grown in different media that produce the same growth rate. Five classes of growth-related parameters that are generally useful in describing or establishing the macromolecular composition of bacterial cultures are described in detail in this review. A number of equations have been reported that describe the macromolecular composition of an average cell in an exponential culture as a function of the culture doubling time and five additional parameters: the C- and D-periods, protein per origin (PO), ribosome activity, and peptide chain elongation rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".