Modelling and simulation of plasmid DNA adsorption on ion‐exchange membrane columns
Bibliographic record
Abstract
Abstract A transport model that considers convection, diffusion, and finite kinetic rate on the membrane column as well as the influence of extra column dispersion and lag times, was used in this study to mathematically describe the frontal adsorption behaviour of plasmid DNA in an ion‐exchange membrane chromatography system. The corresponding partial differential equations system was solved using the numerical method of lines (MOL) on a MATLAB platform. Experimental data from literature describing the frontal adsorption of pCI DNA in an ion‐exchange membrane column was used as a model system for validation of MOL solution of the transport model. This approach results in a unique way to predict frontal performance and it is a valuable tool to assess the use of membrane adsorbers in large scale processes for plasmid purification. Un modèle de transport qui considère la convection, la diffusion et la cinétique finie sur la colonne de membrane ainsi que l'influence de la dispersion supplémentaire de colonne et les délais de réponse, a été utilisé dans cette étude pour décrire mathématiquement le comportememt d'adsorption frontale de l'ADN plasmidique dans un système de chromatographie à membrane échangeuse d'ions. Le système correspondant d'équations différentielles partielles a été résolu en utilisant la méthode numérique des lignes sur une plate‐forme MATLAB. Des données expérimentales de la littérature décrivant l'adsorption frontale de l'ADN pCI dans une colonne à membrane échangeuse d'ions ont été utilisées comme système modèle pour la validation d'une solution MOL pour le système de transport. Cette approche aboutit à un moyen unique de prévoir la performance frontale et c'est un outil de valeur pour évaluer l'utilisation des adsorbants à membrane dans des procédés à grande échelle pour la purification des plasmides. © 2010 Canadian Society for Chemical Engineering
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".