Self‐cycling operation increases productivity of recombinant protein in <i>Escherichia coli</i>
Bibliographic record
Abstract
Self-cycling fermentation (SCF), a cyclical, semi-continuous process that induces cell synchrony, was incorporated into a recombinant protein production scheme. Escherichia coli CY15050, a lac(-) mutant lysogenized with temperature-sensitive phage λ modified to over-express β-galactosidase, was used as a model system. The production scheme was divided into two de-coupled stages. The host cells were cultured under SCF operation in the first stage before being brought to a second stage where protein production was induced. In the first stage, the host strain demonstrated a stable cycling pattern immediately following the first cycle. This reproducible pattern was maintained over the course of the experiments and a significant degree of cell synchrony was obtained. By growing cells using SCF, productivity increased 50% and production time decreased by 40% compared to a batch culture under similar conditions. In addition, synchronized cultures induced from the end of a SCF cycle displayed shorter lysis times and a more complete culture-wide lysis than unsynchronized cultures. Finally, protein synthesis was influenced by the time at which the lytic phase was induced in the cell life cycle. For example, induction of a synchronized culture immediately prior to cell division resulted in the maximum protein productivity, suggesting protein production can be optimized with respect to the cell life cycle using SCF.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".