Inferring bistable lac operen Boolean regulatory networks using evolutionary computation
Bibliographic record
Abstract
The lac operen in E. coli is one of the earliest examples of an inducible system of genes being under both positive and negative control that is capable of showing bistability. In this paper, we present a methodology to infer synthetic threshold Boolean regulatory networks of a reduced model of the lac operon using evolutionary computation. The formulation consists in a vector representation of the solutions (networks) and a fitness function specially designed to correctly simulate the bistability through the models' fixed points. We compared the effectiveness and efficiency (runtime) of the proposed approach using three evolutionary computation techniques: differential evolution, genetic algorithms, and particle swarm optimization. The results showed that the three algorithms are capable of finding solutions, being differential evolution the most effective, whereas genetic algorithms was the least effective and efficient in terms of runtime. Particle swarm optimization obtained a good trade-off between effectiveness versus efficiency. One of the inferred solutions was analyzed showing some interesting biological insights, as well as correctly being able to model bistability without any spurious attractors. Overall, the proposed formulation was effective to infer bistable lac operon models under the threshold Boolean network paradigm.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".