Origin of Self-Replicating Biopolymers: Autocatalytic Feedback Can Jump-Start the RNA World
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Life is based on biopolymers that have the ability to replicate themselves. Here we consider how a self-replicating RNA system may have originated. We consider a reaction system in which polymerization is possible by the addition of an activated monomer to the end of a chain. We suppose that a small fraction of polymers longer than some minimum length L have the ability to act as polymerase ribozymes. Polymerization can occur spontaneously at a slow rate and can also be catalyzed by polymerase ribozymes, if these ribozymes exist. The system contains autocatalytic feedback: increasing the polymerization rate causes the ribozyme concentration to increase, which causes the polymerization rate to further increase. For an infinite volume, the dynamics are deterministic. There are two stable states: a ‘dead’ state with a very low concentration of ribozymes and a polymerization rate almost equal to the spontaneous rate, and a ‘living’ state with a high concentration of ribozymes and a high rate of polymerization occurring via ribozyme catalysis. In a finite volume, such as the interior of a lipid vesicle or other small compartment, the reaction dynamics is stochastic and concentration fluctuations can occur. Using a stochastic simulation, we show that if a small number of ribozymes is initially formed spontaneously, this can be enough to drive the system from the dead to the living state where ribozyme-catalyzed synthesis of large numbers of additional ribozymes occurs. This transition occurs most easily in volumes of intermediate size.
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.
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) | 0.001 | 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 it