Autocatalytic Replication and Homochirality in Biopolymers: Is Homochirality a Requirement of Life or a Result of It?
Bibliographic record
Abstract
A key step in the origin of life is the establishment of autocatalytic cycles controlled by biopolymer catalysts. These catalysts (either ribozymes or proteins) are composed of homochiral monomers. Homochirality in living systems is maintained because biopolymers are asymmetric in their catalysis and synthesize molecules of their own handedness. Asymmetric autocatalysis is also possible with small molecules, as demonstrated by the Soai reaction, but it is rare. As far as we know, single nucleotides and amino acids are not autocatalytic. The observation that organic molecules in meteorites can have an enantiomeric excess of a few percent suggests that the prebiotic mixture may have had a partial chiral bias that was caused by external physical influences. Here, we consider the way that such a partial prebiotic bias would influence the origin of ribozymes in an RNA world scenario. We have previously shown how a transition to a living state can occur in a model for RNA polymerization. Here, we add chirality to the problem by considering simultaneous synthesis and polymerization of left- and right-handed monomers. The two chemical synthesis rates may be equal or unequal, due to physical or chemical effects prior to the origin of life. We determine the stationary states of this reaction system. The nonliving state is racemic, or slightly biased. There are two living states that are almost completely homochiral, whether or not the nonliving state is biased. It is a feature of our model that, for some regions of parameter space, living and nonliving states are both found to be stable under the same conditions. The origin of life therefore involves a stochastic transition between the nonliving and living states. Our model extends previous theories by treating the origin of life and the origin of chirality as aspects of the same model.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".