A transistor model for the cystic fibrosis transmembrane conductance regulator
Bibliographic record
Abstract
In this paper we present a transistor circuit model for cystic fibrosis transmembrane conductance regulator (CFTR) that seeks to map the functional form of CFTR both in wild type and mutants. The circuit architecture is configured so that the function, and as much as possible the form, faithfully represents what is known about CFTR from cryo-electron microscopy and molecular dynamics. The model is a mixed analog-digital topology with an AND gate receiving the input from two separate ATP-nucleotide-binding domain binding events. The analog portion of the circuit takes the output from the AND gate as its input. The input to the circuit model and its noise characteristics are extracted from single-channel patch-clamp experiments. The chloride current predicted by the model is then compared with single-channel patch-clamp recordings for wild-type CFTR. We also consider the patch-clamp recordings from CFTR with a G551D point mutation, a clinically relevant mutant that is responsive to therapeutic management. Our circuit model approach enables bioengineering approaches to CFTR and allows biophysicists to use efficient circuit simulation tools to analyze its behavior.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".