Engineered gene networks enable non‐genetic drug resistance and enhanced cellular robustness
Bibliographic record
Abstract
Drug resistance complicates the treatment of cancer and infectious diseases, and often arises from the elevated expression of a gene that neutralises or reduces drug activity. To investigate this and other expression‐based mechanisms of drug resistance, the authors engineered a set of gene regulatory networks in the eukaryotic model organism Saccharomyces cerevisiae to control a homologue of the cancer‐related human multidrug resistance gene MDR1 . Using this system, they explored experimentally how different gene regulatory network features, also called genetic network motifs, contribute to gene expression dynamics and cellular fitness. They observed that coherent feedforward and positive feedback motifs enable rapid and self‐sustained activation of gene expression, and enhance cell survival in the presence of a cytotoxic drug. These observations underscore that genetic network motifs can be critical for drug resistance and that genetic network engineering can be used to enhance cellular tolerance to cytotoxins or other environmental stresses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".