Computer simulations of the phase separation in model membranes
Bibliographic record
Abstract
We used computer simulations to investigate the properties of model lipid membranes with coexisting phases. This is relevant for understanding lipid-lipid interactions underlying lateral organization in biological membranes. Molecular dynamics simulations with the MARTINI coarse-grained force field were employed to study lipid bilayers -40 nm in lateral dimension on a 20 micros time scale. The simulations retain near atomic-level detail and lipid chemical specificity, and allow formation of multiple domains of tens of nanometers in size. Using ternary lipid mixtures of saturated and unsaturated lipids and cholesterol, we reproduced the coexistence of the Lalpha/gel phases and the Lo/Ld phases. Phase transformation proceeded by either nucleation or spinodal decomposition. The properties of coexisting phases were characterized in detail, including partial lipid areas, composition, phase boundary and domain registry, based on Voronoi tessellation. We investigated variations of these properties with temperature and surface tension, and compared them to our recent simulations of lipid monolayers of the same size and composition. We found substantial overlap in bilayer and monolayer properties. Increasing the temperature in bilayers produced similar effects as increasing the surface tension in monolayers. This information can be used for interpreting experimental data on model membranes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".