Small delay, big waves: a minimal delayed negative feedback model captures <i>Escherichia coli</i> single cell SOS kinetics
Bibliographic record
Abstract
BACKGROUND: How exactly does an organism coordinate its responses to differing environmental conditions, especially when several responses and physiological priorities are potentially conflicting? Recently, single cell results have been published on the kinetics of the bacterial SOS response. Based on these, we construct a relatively simple mathematical model for the regulatory control of the mutagenic elements of the Escherichia coli DNA repair system. METHODS: We employ one first order delay differential equation for the dynamics of the activation level of mutagenic gene repair and one first order ordinary differential equation for the dynamics of the level of DNA damage. After manual adjustment of parameters, our model qualitatively reproduces the UV dose dependent RecA expression peak occurrence, peak amplitude and peak timing. Parameter noise captures qualitatively the fluctuations observed in the experimental data. Quantitative agreement is achieved for timing of the three response peaks for different doses of UV. CONCLUSIONS: A delayed negative feedback is likely to play a primary role in the regulation of the E. coli mutagenic gene repair. The model presented in this paper is an example of how a delayed regulatory mechanism establishes control over a critical organismic response with negative secondary effects.
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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 | 0.001 |
| 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) | 0.002 | 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".