Parametric evolution of a bacterial signalling system formalized by membrane computing
Bibliographic record
Abstract
In this work, we use particle swarm optimization (PSO) to adjust the parameters of a membrane computing (MC) model of a synthetic autoinducer-2 (AI-2) signalling system in genetically engineered Escherichia coli bacteria. Bacteria release, receive and recognize signalling molecules in order to exchange information. These signalling molecules are responsible for coordinating gene expression at the population level in response to various stimuli such as size of the population, nutrient availability and other biochemical signals. This bacterial cell-to-cell communication is known as Quorum Sensing (QS). AI-2, from Vibrio harveyi, is the signaling molecule of interest in this study. We present a non-deterministic in silico model of Autoinducer-2 Quorum Sensing that is formalized by membrane computing (MC). The model is driven by 23 interaction rules that define biochemical interactions between independent compartments known as membranes. Due to the high dimensionality of this problem as well as lack of data relating to the biochemical parameters of this signalling system, we used a generic particle swarm optimization (PSO) algorithm to discover optimal solutions for the rule stochasticity constants. Our results were compared to the expected trends in quorum sensing behaviour. Ultimately, the results obtained from the PSO are thought to be in accordance with the predicted behaviour of the synthetic AI-2 signalling system.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".