Analyzing Enzyme Kinetic Data Using the Powerful Statistical Capabilities of R
Bibliographic record
Abstract
Abstract We describe a powerful tool for enzymologists to use for typical non-linear fitting of common equations in enzyme kinetics using the statistical program R. Enzyme kinetics is a powerful tool for understanding enzyme catalysis, regulation, and inhibition but tools to perform the analysis have limitations. Software to perform the necessary nonlinear analysis may be proprietary, expensive or difficult to use, especially for a beginner. The statistical program R is ideally suited to analyzing enzyme kinetic data; it is free in two respects: there is no cost and there is freedom to distribute and modify. It is also robust, powerful and widely used in other fields of biology. In this paper we introduce the program R to enzymologists who want to analyze their data but are unfamiliar with R or similar command line statistical analysis programs. Data are inputted and examples of different non-linear models are fitted. Results are extracted and plots are generated to assist judging the goodness of fit. The instructions will allow users to create their own modifications to adapt the protocol to their own experiments. Because of the use of scripts, a method can be modified and used to analyze different datasets in less than one hour.
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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 | 0.024 | 0.100 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.031 |
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".