Measuring intracellular enzyme concentrations
Bibliographic record
Abstract
Enzymology is one of the fundamental areas of biochemistry and involves the study of the structure, kinetics, and regulation of enzyme activity. Research in this area is often conducted with purified enzymes and extrapolated to in vivo conditions. The specificity constant, k(S) , is the ratio between k(cat) (the catalytic constant) and K(m) (Michaelis-Menten constant), and expresses the efficiency of an enzyme as a catalyst. This parameter is usually determined for purified enzymes, and in this work, we propose a classroom experiment for its determination in situ, in permeabilized yeast cells, based on a method of external enzyme addition, which was previously reported. Under these conditions, which resemble the in vivo state, enzyme concentrations and protein interactions are preserved. The students are presented with a novel approach in enzymology, based on the titration methods that allow the measurement of the enzyme amount, and thus the k(cat) and k(S) . The method will also be used to investigate the effect of exposure to oxidative stress conditions on yeast glyoxalase I.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".