Simulation-Based Methods for Optimal Sampling Design in Systems Biology
Bibliographic record
Abstract
In many research areas of systems biology including virology, pharmacokinetics, and population biology, researchers often use the dynamical systems to describe dynamics of biological systems. We can learn about these biological systems by estimating the parameters of the dynamical systems from sampling data. Therefore, an important question arises: how can one select the optimal sampling points for accurately estimating the parameters? Classical methods often rely on Fisher information matrix-based criteria such as A-, D-, and E-optimality. However, these methods require an initial estimate of the parameters and often lead to a suboptimal result when the initial estimate is inaccurate. In this paper, we develop two simulation-based methods for optimal sampling design that do not require an initial estimate of the parameters. The first method, E-optimal-ranking (EOR), employs the E-optimal criterion while the second method utilizes the Long short-term memory (LSTM) neural network. We demonstrate the performance of our proposed methods using simulation studies based on two popular models in system biology (Lotka-Volterra and Three-compartment models). The results show that our methods outperform the naive random selection method and the classical E-optimal design.
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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 | 0.001 | 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".