Methods to Estimate Cryptic Sequence Complexity
<|reference_start|>Methods to Estimate Cryptic Sequence Complexity: Complexity is a signature quality of interest in artificial life systems. Alongside other dimensions of assessment, it is common to quantify genome sites that contribute to fitness as a complexity measure. However, limitations to the sensitivity of fitness assays in models with implicit replication criteria involving rich biotic interactions introduce the possibility of difficult-to-detect ``cryptic'' adaptive sites, which contribute small fitness effects below the threshold of individual detectability or involve epistatic redundancies. Here, we propose three knockout-based assay procedures designed to quantify cryptic adaptive sites within digital genomes. We report initial tests of these methods on a simple genome model with explicitly configured site fitness effects. In these limited tests, estimation results reflect ground truth cryptic sequence complexities well. Presented work provides initial steps toward development of new methods and software tools that improve the resolution, rigor, and tractability of complexity analyses across alife systems, particularly those requiring expensive in situ assessments of organism fitness.<|reference_end|>
@article{moreno2024methods,
    title={Methods to Estimate Cryptic Sequence Complexity},
    author={Matthew Andres Moreno},
    journal={arXiv preprint arXiv:2404.10854},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10854},
    primaryClass={q-bio.PE cs.NE}
}