Molecular Errors, Cryptic Sequences, and Evolvability

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Model shows a speed–accuracy tradeoff driving an either/or outcome: cells evolve either low error rates with broad proofreading or high error rates that purge toxic cryptic sequences. Translation read-through (~20–30% estimated proteome impact) is studied as a concrete example; selection against harmful extensions plus compensatory evolution generates a pool of benign cryptic variants. Ribosome profiling finds >50% of annotated noncoding transcripts ribosome-associated and one clear 28–amino‑acid ORF; the high-error attractor boosts evolvability by pre-screening and relies on neighborhood richness rather than standing polymorphism.

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" chapters (21)

1. 0:00Intro
2. 0:25Gene expression
3. 1:19Outline
4. 5:16Distribution of fitness effects of new mutations
5. 5:36Stop codon readthrough: case study of molecular errors
6. 6:30Mutation bias favors misfolding
7. 7:17Selection for a stable fold even after a readthrough error
8. 8:52Costs and benefits of proofreading
9. 13:35Two attractors for a range of population sizes (i.e. range of limits to weak selection)
10. 14:56Model applies to many kinds of molecular errors
11. 18:54Environmental change in optimal trait value
12. 19:07Populations with high error rates evolve faster
13. 27:04Let's look at cryptic sequences with and without genetic diversity
14. 28:16Relaxed selection → cryptic genetic diversity
15. 28:56Genotype space / neutral network
16. 29:57Multiple cryptic loci provide more adaptive options, even in the absence of genetic diversity across population
17. 30:24Two ways to access more novel phenotypes: genetic polymorphism or neighborhood richness
18. 31:50Each cryptic sequence affects multiple traits
19. 35:03Compensatory evolution drives high neighborhood richness
20. 35:47"Spread" across a genotype space is not required for the high evolvability of polygenic traits in asexuals
21. 43:03Are "non-coding" transcripts associated with ribosomes?