Complexity analysis and optimal experimental design for parameter estimation of biological systems
Why this work is in the frame
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Bibliographic record
Abstract
A biological system describes the dynamics of a biological process and can be modeled by a group of nonlinear differential equations. The nonlinear terms in differential equations of a biological system result from the reaction rates which can not be measured and yet contain important parameters to be identified. In practice, not all states are measured because of the limitation of experimental conditions and cost. In this paper, we develop a methodology for complexity analysis and optimal experimental design to maximize the number of identifiable parameters in the reaction rates while minimizing the number of states which must be measured. We use the model of the programmed cell death or apoptosis as an instance to illustrate the proposed method. The analysis shows that the results from our proposed method are better than those from the existing methods.
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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 it