Optimal control for context-sensitive probabilistic Boolean networks with perturbation using probabilisitic model checking
Why this work is in the frame
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Bibliographic record
Abstract
A context-sensitive probabilistic Boolean network with perturbation (CS-PBNp) closely models gene regulatory networks under external controls that alter the evolution of the networks in a desirable way over a finite time horizon. In this paper, we consider optimal control for a CS-PBNp, proposing an approach, based on a formal verification technique - probabilistic model checking, for finding optimal control policy that minimizes the expected cost over the entire control horizon. To this end, we first present a detailed procedure of modeling a CS-PBNp using the modeling language of a widely used probabilistic model checker PRISM. Furthermore, by analyzing computation of reward-based temporal properties, we provide a reduction approach allowing us to formulate the optimal control problem as minimum reachability reward properties. Based on this result, we incorporate control and state cost information into the PRISM code of a CS-PBNp such that automated model checking a minimum reachability reward property on the code gives the solution to the optimal control problem. Experiment results on an apoptosis network demonstrate the feasibility and effectiveness of our approach.
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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