Mathematical modeling of the <i>apo</i> and <i>holo</i> transcriptional regulation in <i>Escherichia coli</i>
Bibliographic record
Abstract
Transcription factors (TFs) modulate gene expression as a consequence of internal or exogenous changes in cell signaling. TFs can bind to DNA either with their effector bound (holo conformation), or as free proteins (apo conformation). With the aim of contributing to the understanding of the evolutionary fitness and organizational principles behind the different TF conformations, we inquire into the origins of these conformational differences by analyzing these two TF conformations from the perspective of Savageau's demand theory. For the control of a gene whose function is in high demand, we found that evolutionary constraints are responsible for activator TFs binding to DNA mainly in holo conformation whereas apo activation is under-represented. The mathematically controlled comparison of the apo and holo conformations reveals formal and evolutionary arguments in favor of this TF control asymmetry, which suggests that evolution favors holo activation under environmental conditions commonly found by E. coli in the human digestive tract. Specifically, the sensibility analysis performed for the holo conformation, in the positive mode of regulation, shows that the wild-type is more robust for situations where realizable changes in the model's parameters favored a better performance under non-stressful environmental conditions commonly found by E. coli in the human digestive tract. By contrast, the positive apo conformation is better adapted to adverse situations. On the other hand, the sensibility analysis performed for the negative mode of regulation showing none of the TF active conformations presents an advantage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".