Population-Level Membrane Diversity Triggers Growth and Division of Protocells
Bibliographic record
Abstract
To date, multiple mechanisms have been described for the growth and division of model protocells, all of which exploit the lipid dynamics of fatty acids. In some examples, the more heterogeneous aggregate consisting of fatty acid and diacyl phospholipid or fatty acid and peptide grows at the expense of the more homogeneous aggregate containing a restricted set of lipids with similar dynamics. Imbalances between surface area and volume during growth can generate filamentous vesicles, which are typically divided by shear forces. Here, we describe another pathway for growth and division that depends simply on differences in the compositions of fatty acid membranes without additional components. Growth is driven by the thermodynamically favorable mixing of lipids between two populations, i.e., the system as a whole proceeds toward equilibrium. Division is the result of growth-induced curvature. Importantly, growth and division do not require a specific composition of lipids. For example, vesicles made from one type of lipid, e.g., short-chain fatty acids, grow and divide when fed with vesicles consisting of another type of lipid, e.g., long-chain fatty acids, and vice versa. After equilibration, additional rounds of growth and division could potentially proceed by the introduction of compositionally distinct aggregates. Since prebiotic synthesis likely gave rise to mixtures of lipids, the data are consistent with the presence of growing and dividing protocells on the prebiotic Earth.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".