Ferricyanide Reduction by <i>Escherichia </i><i>c</i><i>oli</i>: Kinetics, Mechanism, and Application to the Optimization of Recombinant Fermentations
Bibliographic record
Abstract
Ferricyanide reduction was studied by flow injection analysis (FIA) and chronoamperometry (CA) using two host strains and one recombinant strain of E. coli. Samples taken from batch cultures of E. coli JM105 and HB101 showed maximal specific ferricyanide reduction rates in the late exponential phase of growth, with values (micromol/min x g) of 24 (FIA) and 17 (CA) for JM105, and 36 (FIA) for HB101, when shake-flask cultures were sampled, and 70 for HB101, when a chemostat was used to control pH and dissolved oxygen concentration throughout the cultivation. Remarkably higher ferricyanide reduction rates were obtained with HB101 cells cultivated continuously at very slow growth rate, when chilled, resuspended cell samples were incubated for 5 min in solutions containing 10 mM succinate or formate. These compounds are substrates for primary, membrane-bound dehydrogenases that transfer electrons via ubiquinone to the cytochrome oxidase complexes. Apparent Michaelis-Menten kinetics were observed with respect to ferricyanide concentration when 10 mM succinate was included in the assay buffer; apparent Km values of 10.1+/-0.6 mM and 14.4+/-1.2 mM ferricyanide were obtained for exponential- and stationary-phase E. coli JM105, respectively. Cyanide inhibition studies show that ferricyanide is reduced mainly by cytochrome o oxidase in exponentially growing cells. The large difference in ferricyanide reduction rates observed in the absence and presence of succinate and formate were used to signal stationary-phase entry 5 h after induction of recombinant human Cu/Zn superoxide dismutase expression in a batch fermentation of E. coli HMS174(DE3)(pET3ahSOD). This new method can be used as an adjunct to the quantitation of medium components for the optimization of recombinant fermentations.
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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) | 0.001 | 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".