Extracting Kinetics from Affinity Capillary Electrophoresis (ACE) Data: A New Blade for the Old Tool
Bibliographic record
Abstract
We describe a mathematical approach that enables extraction of kinetic rate constants from thousands of studies conducted over the past two decades with affinity capillary electrophoresis (ACE). Previously, ACE has been used almost exclusively for obtaining equilibrium constants of intermolecular interactions. In this article, we prove that there exists an analytical solution of partial differential equations describing mass transfer in ACE. By using an in silico study, we demonstrate that the solution is applicable to experimental conditions that are typically used in ACE and found in most historical ACE experiments. The solution was validated by extracting rate constants from previously published ACE data and closely matching independently obtained results. Lastly, it was used to obtain previously unknown rate constants from historical ACE data. The new mathematical approach expands the applicability of ACE to a wider range of biomolecular interactions and enables both prospective and retrospective data analysis. The obtained kinetic information will be of significant practical value to the fields of pharmacology and molecular biology.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".