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Methods to Estimate Cryptic Sequence Complexity

Matthew Andres Moreno

TL;DR

The paper tackles the problem of quantifying cryptic sequence complexity in artificial life by introducing three knockout-based assays that target additive, epistatic, and any-effect cryptic sites in digital genomes. It employs dose-response modeling with a negative binomial distribution, skeletonization with jackknife analysis, and Burnham-Overton capture-recapture methods to estimate hidden adaptive sites. Across initial simulations, additive estimates near ground truth (e.g., 30 vs 50 additive sites with mean effect around 0.5), substantial recovery of epistatic sites (80/94), and close total any-effect estimates (558 vs 555; 95% CI 533–583) demonstrate potential for improved resolution and rigor in cryptic complexity analyses. These results, coupled with proposed software tools, point toward scalable, in situ assessment frameworks for complexity in artificial-life research and motivate further validation across diverse systems.

Abstract

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.

Methods to Estimate Cryptic Sequence Complexity

TL;DR

The paper tackles the problem of quantifying cryptic sequence complexity in artificial life by introducing three knockout-based assays that target additive, epistatic, and any-effect cryptic sites in digital genomes. It employs dose-response modeling with a negative binomial distribution, skeletonization with jackknife analysis, and Burnham-Overton capture-recapture methods to estimate hidden adaptive sites. Across initial simulations, additive estimates near ground truth (e.g., 30 vs 50 additive sites with mean effect around 0.5), substantial recovery of epistatic sites (80/94), and close total any-effect estimates (558 vs 555; 95% CI 533–583) demonstrate potential for improved resolution and rigor in cryptic complexity analyses. These results, coupled with proposed software tools, point toward scalable, in situ assessment frameworks for complexity in artificial-life research and motivate further validation across diverse systems.

Abstract

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.
Paper Structure (5 sections, 1 figure)

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Introduction
  2. Additive-effect Cryptic Sites
  3. Epistatic-effect Cryptic Sites
  4. Any-effect Cryptic Sites
  5. Conclusion

Figures (1)

  • Figure 1: Distinguishing between small-effect and epistatic genome sites. Epistatic sites exhibit both 1) severe fitness effects when knocked out individually from "skeletonized" minimal viable genomes (i.e., "jackknifed") and 2) are often absent from sampled "skeleton" genomes (i.e., high exclusion rates). Minor jitter added to points for clarity.