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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".