Kinetic model parameter estimation using genetic algorithms of the oxidation of phenol in water catalyzed by the laccase enzyme for the design of a biosensor
Bibliographic record
Abstract
Predicting the dynamic response of key processes that take place in industrial biochemical systems such as the measurement of operating parameters by means of biosensors, before the biosensors themselves are prototyped, is of utmost importance. The advantages of performing mathematical modeling of biosensor systems in their development stages include cost reduction, easier response tuning and faster performance optimization. In most cases, the mathematical models for enzyme kinetics depend on multiple parameters. Finding the numerical values of such parameters usually requires carrying out a vast number of experiments, which is time intensive, expensive and involves the usage of specialized laboratory equipment. This work proposes the utilization of genetic algorithms as an alternative methodology for kinetic model parameter estimation of the oxidation of phenol in water, catalyzed by the laccase enzyme. The corresponding kinetics mathematical model of the oxidation reaction is used as a case study, to compare the results obtained using the genetic algorithms approach proposed with those found in the literature. The algorithm estimated the values of several parameters of the model, such as reaction rate constants, rate constant of transformation of oxygen by the electrode and stoichiometric coefficient, among others. The results found in this investigation by means of genetic algorithms show an agreement of 91%–99% with the data available in the literature. This approach also proved to be more accurate than the basic polynomial regression estimation method, which is commonly used and was implemented for comparison purposes. The proposed technique for parameters estimation in enzyme reaction models enabled the design of a phenol biosensor for concentrations ranging from 5 to 30 ppm. This technique has a high potential of application in the biosensor industry because of its cost savings, high speed and good accuracy.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".