COMPUTER SIMULATIONS OF TRANSPORT THROUGH MEMBRANES: PASSIVE DIFFUSION, PORES, CHANNELS AND TRANSPORTERS
Bibliographic record
Abstract
A key function of biological membranes is to provide mechanisms for the controlled transport of ions, nutrients, metabolites, peptides and proteins between a cell and its environment. We are using computer simulations to study several processes involved in transport. In model membranes, the distribution of small molecules can be accurately calculated; we are making progress towards understanding the factors that determine the partitioning behaviour in the inhomogeneous lipid environment, with implications for drug distribution, membrane protein folding and the energetics of voltage gating. Lipid bilayers can be simulated at a scale that is sufficiently large to study significant defects, such as those caused by electroporation. Computer simulations of complex membrane proteins, such as potassium channels and ATP-binding cassette (ABC) transporters, can give detailed information about the atomistic dynamics that form the basis of ion transport, selectivity, conformational change and the molecular mechanism of ATP-driven transport. This is illustrated in the present review with recent simulation studies of the voltage-gated potassium channel KvAP and the ABC transporter BtuCD.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".