Effects of protein maturation on the noise in gene expression
Bibliographic record
Abstract
Fluorescent proteins are frequently used as reporters for gene expression in living cells, either by being expressed in tandem with a protein of interest or through the creation of fusion proteins. The data yielded by the fluorescence output are of considerable interest in efforts to formulate quantitative models of cellular behavior underway in fields such as systems biology and synthetic biology. An often neglected aspect of these proteins, however, is their maturation: Before a fluorescent protein can generate a fluorescent signal, it must mature through a series of steps (folding, cyclization, and oxidation) that may take from many minutes to over a day. The presence of these maturation steps creates a distinction between the observed gene expression profile and the actual profile. We examine this effect through a simplified gene expression model and conclude that fluorescent protein maturation can have significant effects on estimates of both the mean protein levels and the variability in gene expression. The model shows that in many regimes, the observed variability will be increased by the maturation process, but indicates the existence of regimes in which the observed variability will actually be less than the true variability of the target protein. The latter effect arises from a low-pass filtering effect introduced by the chain of maturation reactions. The results suggest that the maturation of fluorescent proteins should be taken into account when using such proteins as quantitative indicators of gene expression levels.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".