Dynamic Modeling of Antimicrobial Pore Formation in Engineered Tethered Membranes
Bibliographic record
Abstract
In this paper, a mesoscopic-to-observable dynamic model of the pore formation measurement platform is presented. The platform is composed of a controllable engineered tethered membrane. Using the mesoscopic-to-observable model and experimental measurements allows the platform to be used to gain insight into the pore formation dynamics of peptides in biological membranes. These results are useful for the development of novel drugs, gene delivery therapies, and controlling pore formation in cell-like bioreactors. The model consists of coarse-grained molecular dynamics, a continuum model composed of a generalized version of Fick's law of diffusion coupled with surface reaction-diffusion equations, and a fractional order macroscopic model. We consider the pore formation dynamics of the antimicrobial peptide PGLa using the dynamic model and experimental measurements from the platform. The results provide a possible reaction-mechanism for PGLa pore formation in charged and uncharged membranes, which accounts for binding, translocation, and oligomerization of PGLa. The reaction-mechanism suggests that PGLa not only increases the number of pores in negatively charged membranes, but also increases the lifetime of pores compared to PGLa pores in uncharged membranes. Though results for PGLa are presented, the dynamics model and platform can be used to investigate the pore formation dynamics of other peptides.
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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 | 0.000 | 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 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".