Dynamic control of arabinose and xylose utilization in<i>E. coli</i>
Bibliographic record
Abstract
Abstract The common bacteriumEscherichia coli(E. coli) can utilize the pentose sugars arabinose and xylose for growth and energy. When fed both these sugars, the bacterium preferentially utilizes arabinose and only when all the arabinose is exhausted from the media does it start to use xylose. This hierarchical utilization of the two sugars is dictated by two proteins: AraC and XylR. These proteins act as controllers of sugar utilization and dictate the timing and rate of utilization of these sugars. While the biochemical interactions defining individual arabinose and xylose utilization systems are well understood, it is not completely understood how the hierarchical utilization is maintained by the bacterium, and how the regulatory crosstalk between the two systems facilitates this hierarchy. To help answer these questions, in this work, we systematically experimentally characterize the regulatory crosstalk between the two sugar utilization systems. Our work demonstrates extensive interaction between the two sugar systems. Specifically, data from our experiments suggest that the xylose system can regulate arabinose gene expression and consequently, cellular physiology dynamically via promiscuous transport and maybe through cross interactions between regulator and non‐cognate sugar. Put together, we demonstrate that arabinose and xylose utilization networks exhibit an example of distributed control in a biological system. This design likely ensures that the system does not fail under perturbations (mutations). Our results help understand multi‐process control in biological systems and bring to light design criteria for synthetic biology applications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".