Dynamic Modelling and Prediction of Cytotoxicity on Microelectronic cell Sensor Array
Bibliographic record
Abstract
Abstract A real‐time cell electronic sensing (RT‐CES) system has been used for label‐free dynamic measurements of cell responses to toxicant. Cells are grown onto the surfaces of the microelectronic sensors. Changes in cell number expressed as cell index (CI) have been recorded on‐line as time series. The CI data are used for dynamic modelling or parameter estimation for cell cytotoxicity process. We consider two dynamic modelling approaches, namely data‐based system identification and first principle modelling. It is shown that data‐based system identification can provide a quick solution for the cytotoxicity dynamic models and is effective for short‐term predictions. It, however, can be poor for long‐term predictions, particularly if there is no output correction, i.e., when the model is used for simulation. In view of this, the first principle modelling approach by considering fundamental physical principles such as toxicant transport is explored. For long‐term prediction or simulation, the prediction performance for some of cytotoxicity process is dramatically improved using the models obtained from the latter approach. This happens only if the underlying mechanism is truly understood. Through several cytotoxicity modelling and validation studies, it is shown that the black box modelling and first principle modelling both should be considered in challenging modelling problems such as the cytotoxicity. Pros and cons of the two modelling approaches are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".