Using a Péclet number for the translocation of a polymer through a nanopore to tune coarse-grained simulations to experimental conditions
Bibliographic record
Abstract
Coarse-grained simulations are often employed to study the translocation of DNA through a nanopore. The majority of these studies investigate the translocation process in a relatively generic sense and do not endeavor to match any particular set of experimental conditions. In this manuscript, we use the concept of a Péclet number for translocation, P(t), to compare the drift-diffusion balance in a typical experiment vs a typical simulation. We find that the standard coarse-grained approach overestimates diffusion effects by anywhere from a factor of 5 to 50 compared to experimental conditions using double stranded DNA (dsDNA). By defining a Péclet control parameter, λ, we are able to correct this and tune the simulations to replicate the experimental P(t) (for dsDNA and other scenarios). To show the effect that a particular P(t) can have on the dynamics of translocation, we perform simulations across a wide range of P(t) values for two different types of driving forces: a force applied in the pore and a pulling force applied to the end of the polymer. As P(t) brings the system from a diffusion dominated to a drift dominated regime, a variety of effects are observed including a non-monotonic dependence of the translocation time τ on P(t) and a steep rise in the probability of translocating. Comparing the two force cases illustrates the impact of the crowding effects that occur on the trans side: a non-monotonic dependence of the width of the τ distributions is obtained for the in-pore force but not for the pulling force.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".