Origin of Self-Replicating Biopolymers: Autocatalytic Feedback Can Jump-Start the RNA World
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.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".