Bibliographic record
Abstract
Cells contain thousands of different lipids. Their rapid and redundant metabolism, dynamic movement, and many interactions with other biomolecules have justly earned lipids a reputation as a vexing class of molecules to understand. Further, as the cell's hydrophobic metabolites, lipids assemble into supramolecular structures─most commonly bilayers, or membranes─from which they carry out myriad biological functions. Motivated by this daunting complexity, researchers across disciplines are bringing order to the seeming chaos of biological lipids and membranes. Here, we formalize these efforts as "synthetic lipid biology". Inspired by the idea, central to synthetic biology, that our abilities to understand and build biological systems are intimately connected, we organize studies and approaches across numerous fields to create, manipulate, and analyze lipids and biomembranes. These include construction of lipids and membranes from scratch using chemical and chemoenzymatic synthesis, editing of pre-existing membranes using optogenetics and protein engineering, detection of lipid metabolism and transport using bioorthogonal chemistry, and probing of lipid-protein interactions and membrane biophysical properties. What emerges is a portrait of an incipient field where chemists, biologists, physicists, and engineers work together in proximity─like lipids themselves─to build a clearer description of the properties, behaviors, and functions of lipids and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.009 |
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".