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Record W3202533243 · doi:10.26434/chemrxiv.13238402.v1

Population-level Membrane Diversity Triggers Growth and Division of Protocells

2020· preprint· en· W3202533243 on OpenAlexaff
Ö. Duhan Toparlak, Anna Wang, Sheref S. Mansy

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicOrigins and Evolution of Life
Canadian institutionsUniversity of Alberta
FundersEuropean CommissionSimons Foundation
KeywordsProtocellVesicleDivision (mathematics)BiologyPopulationCell divisionBiophysicsChemistryCell biologyBiochemistryMembraneCell

Abstract

fetched live from OpenAlex

To date, multiple mechanisms have been described for the growth and division of model protocells, all of which exploit the cumulative, unidirectional movement of lipids. The aggregate that is more complex grows at the expense of the smaller or less complex aggregate. 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 composition of fatty acid membranes. Growth is driven by the entropically-favored mixing of lipids between two populations. Division is the result of growth-induced curvature. Importantly, growth and division are cyclic and bidirectional, meaning that 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 are possible through the addition of compositionally distinct vesicles. 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.196
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.258
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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