Optimization of biomolecule separation by combining microscale filtration and design‐of‐experiment methods
Bibliographic record
Abstract
There is considerable interest in developing microscale (i.e., high-throughput) methods that enable multiple filtration experiments to be run in parallel with smaller sample amounts and thus reduce the overall required time and associated cost to run the filtration tests. Previous studies to date have focused on simply evaluating the filtration capacity, not the separation performance. In this work, the stirred-well filtration (SWF) method was used in combination with design-of-experiment (DOE) methods to optimize the separation performance for three binary mixtures of bio-molecules: protein-protein, protein-polysaccharide, and protein-DNA. Using the parallel based format of the SWF method, eight constant-flux ultrafiltration experiments were conducted at once to study the effects of stirring conditions, permeate flux, and/or solution conditions (pH, ionic strength). Four separate filtration tests were conducted for each combination of process variables; in total, over 100 separate tests were conducted. The sieving coefficient and selectivity results are presented to match the DOE design format and enable a greater understanding of the effects of the different process variables that were studied. The method described herein can be used to rapidly determine the optimal combination of process factors that give the best separation performance for a range of membrane-based separations applications and thus obviate the need to run a large number of traditional lab-scale tests. Biotechnol. Bioeng. 2016;113: 2131-2139. © 2016 Wiley Periodicals, Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".