Exactly solvable Ogston model of gel electrophoresis. IX. Generalizing the lattice model to treat high field intensities
Bibliographic record
Abstract
Traditionally, the Ogston regime is studied solely in the limit of low field intensities. This explains why the theoretical discussion has focused until now on the relative roles of the fractional volume available to the analyte and the subtleties of the gel architecture. Over the past several years, we have developed a lattice model of gel electrophoresis that has allowed us to revisit the fundamental assumptions of the standard Ogston model. In particular, we demonstrated that the fractional free volume is not the relevant parameter for gel sieving. In this article, we continue the development of this model and we generalize our mathematical approach to treat nonvanishing electric field intensities. To do so, we must revisit the way biased random walks are normally modeled by stochastic processes. Straightforward generalizations based on standard Metropolis-like schemes fail at high field intensities. Moreover, our generalization requires the complete decoupling of the spatial directions parallel and perpendicular to the field direction. We show that our novel theoretical approach makes it possible to calculate exact mobilities in the presence of lattice obstacles. Several two-dimensional examples are then studied, including one that includes topological dead ends that act like traps. In the latter case, we recover results very similar to those reported by Serwer et al. [Biopolymers 29, 1863 (1990)] on the trapping electrophoresis of charged spheres in agarose gels. In the absence of such traps, the mobility is shown to be a very weak function of the electric field, thus validating the historical neglect of the field intensity in the development of obstruction models for the Ogston sieving regime of small analytes. Finally, we describe how the present model could be improved to treat more realistic cases and we discuss the problem of the field dependence of the diffusion coefficient during electrophoresis.
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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".