A theoretical study of an empirical function for the mobility of DNA fragments in sieving matrices
Bibliographic record
Abstract
The separation of DNA fragments by gel electrophoresis has been studied extensively over the last two decades. More recently, similar studies have been carried out to characterize the separation achieved by the current capillary array electrophoresis systems and their sieving polymer solutions. In all cases, at least three different mobility regimes have been shown to exist: the Ogston regime when the radius of gyration of the DNA fragment is smaller than the pore size, the reptation regime when the DNA is larger than the pore size but remains in a random coil conformation, and finally the reptation-with-orientation regime where the DNA orients in the field direction and essentially all resolution is lost. Unfortunately, although theory helps us understand the different regimes and how to properly exploit them, we still have no theory-based general equations that would apply to all regimes. Such equations would be especially useful to analyze data, optimize separation systems and interpolate mobilities to estimate unknown molecular sizes. Recently, van Winkle, Beheshti and Rill (Electrophoresis 2002, 23, 15-19) proposed an intriguing empirical formula that seems to adequately fit the mobility of dsDNA fragments across all three regimes. In this paper, I investigate the relation between this empirical formula and the known theories of gel electrophoresis, and I study the dependence of its fitting parameters upon the experimental conditions. Finally, I examine how this equation may need to be modified to capture the more subtle details predicted by fundamental theories of DNA gel electrophoresis.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".