Monte Carlo simulations of single and coupled synthetic molecular motors
Bibliographic record
Abstract
We use a minimal model to study the processive motion of coupled synthetic molecular motors along a DNA track and we present data from Monte Carlo (MC) computer simulations based on this model. The model was originally proposed by Bromley et al. [HFSP J. 3, 204 (2009)] for studying the properties of a synthetic protein motor, the "Tumbleweed" (TW), and involves rigid Y-shaped motors diffusively rotating along the track while controlled by a series of periodically injected ligand pulses into the solution. The advantage of the model is that it mimics the mechanical properties of the TW motor in detail. Both the average first passage time which measures the diffusive motion of the motors, and the average dwell time on the track which measures their processivity are investigated by varying the parameters of the model. The latter includes ligand concentration and the range and strength of the binding interaction between motors and the track. In particular, it is of experimental interest to study the dependence of these dynamic time scales of the motors on the ligand concentration. Single rigid TW motors were first studied since no previous MC simulations of these motors have been performed. We first studied single motors for which we found a logarithmic decrease of the average first passage time and a logarithmic increase of the average dwell time with increasing ligand concentration. For two coupled motors, the dependence on ligand concentration is still logarithmic for the average first passage time but becomes linear for the average dwell time. This suggests a much greater stability in the processive motion of coupled motors as compared to single motors in the limit of large ligand concentration. By increasing the number of coupled motors, m, it was found that the average first passage time of the coupled motors only increases slowly with m while the average dwell time increases exponentially with m. Thus the stability of coupled motors on the track can be considerably enhanced by their cooperative motion.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".