Optimizing the expression of a monoclonal antibody fragment under the transcriptional control of the <i>Escherichia coli lac</i> promoter
Bibliographic record
Abstract
The expression of a monoclonal antibody Fab fragment in Escherichia coli strain RB791/pComb3, induced with either lactose or isopropyl-beta-D-thiogalactoside (IPTG), was compared to determine if lactose might provide an inexpensive alternative to induction with IPTG. Induction of Fab expression imposed a metabolic load on the recombinant cells, resulting in lower final cell yields compared to the non-induced controls. An IPTG concentration of 0.05 mM was sufficient to achieve maximal expression of soluble Fab protein when inducing in the early-, mid-, or late-log phases of batch cultures grown using either glucose or glycerol as a carbon source. The largest overall yield of Fab fragments when using 0.05 mM IPTG was achieved by increasing the final yield of cells through glycerol feeding following induction in late-log phase. Lactose was as effective as IPTG for inducing Fab expression in E. coli RB791/pComb3. The greatest overall level of Fab expression was found when cells grown on glycerol were induced with 2 g/L lactose in late-log phase. Since the cost of 0.05 mM of IPTG is significantly greater than the cost of 2 g/L lactose, lactose provides an inexpensive alternative to IPTG for inducing the expression of Fab fragments, and possibly other recombinant proteins, from the E. coli lac promoter.
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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 | 0.000 |
| 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 | 0.001 | 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 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".