Biomolecule transport across biomembranes in the presence of crowding: Polymer translocation driven by concentration and disorder gradients
Bibliographic record
Abstract
The transport of biomolecules across biomembranes occurs in a complex environment where the fluid on both sides of the membrane contains many inclusions. We investigate the translocation of a polymer through a nanopore in such crowded environments via computer simulations. Modeling intracellular and extracellular inclusions as spherical obstacles, the emergence of an entropic driving force is demonstrated for (i) a gradient in the number of obstacles on either side of the pore and (ii) having the obstacles in an ordered arrangement on one side and disordered on the other. With a directional preference of ≈90% of events occurring towards the disordered side and a reduction in the translocation time of ≈30%, the latter case represents a manifestation of entropic trapping. Simple estimates for the magnitude of the driving force are developed for both cases. The effect of allowing the obstacles to move as well as systems containing opposing concentration and disorder gradients are also explored. Demonstrating a robust conclusion that translocation to lower obstacle densities and higher degrees of disorder will be accelerated, these results suggest that accounting for such effects will be critical in developing a realistic model of in vivo translocation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".