Developing a three‐dimensional animation for deeper molecular understanding of michaelis–menten enzyme kinetics
Bibliographic record
Abstract
The mathematical models that describe enzyme kinetics are invaluable predictive tools in numerous scientific fields. However, the daunting mathematical language used to describe kinetic behavior can be confusing for life science students; they often struggle to conceptualize and relate the mathematical representations to the molecular phenomena occurring at both macroscopic and microscopic levels. Students with less developed abstract and mathematical thinking skills may benefit from a visual learning approach. The paucity of visual resources for enzyme kinetics makes this a fertile field for developing novel learning media. We discuss developing a three-dimensional animation aimed at introducing key concepts of Michaelis-Menten enzyme kinetics to undergraduate life science students. This animation uses both realistic and metaphoric depictions of the underlying molecular players, environments, and interactions in enzyme kinetics to contextualize and explain the relationship between the mathematical models and underlying molecular systems. The animation can be viewed at bit.ly/michaelis-menten. © 2018 International Union of Biochemistry and Molecular Biology, 46(5):561-565, 2018.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".