Riehl, M. | bioRxiv Science

bioRxiv2025

Accessibility of Modified NK Fitness Landscapes

In this paper we present two modifications of traditional NK fitness landscapes, the{theta} NK and HNK models, and explore these modifications via accessibility and ruggedness. The{theta} NK model introduces a parameter{theta} to integrate local Rough Mount Fuji-type correlations in subgenotype contributions, simulating more biologically realistic correlated fitness effects. The HNK model incorporates gene regulation effects by introducing a masking mechanism where certain loci modulate the expression of other loci, simulating effects observed in gene regulatory networks without modeling the full network. Through extensive simulations across a wide range of parameters (N, K,{theta} , and H), we analyze the impact of these modifications on landscape accessibility and the number of local optima. We find that increasing{theta} or the number of masking loci (H) generally enhances accessibility, even in landscapes with many local optima, showing that ruggedness doesnt necessarily hinder evolutionary pathways. Additionally, distinct interaction patterns (blocked, adjacent, random) lead to different observations in accessibility and optimum structure. While more complex than traditional NK, we believe each model provides a new biologically relevant facet to fitness landscapes and provides insight into how genetic and regulatory structures influence the evolutionary potential of populations.

evolutionary biology↗

bioRxiv2024

The R-loop Grammar predicts R-loop formation under different topological constraints

R-loops are transient three-stranded nucleic acids that form during transcription when the nascent RNA hybridizes with the template DNA, freeing the DNA non-template strand. There is growing evidence that R-loops play important roles in physiological processes such as control of gene expression, and that they contribute to chromosomal instability and disease. It is known that R-loop formation is influenced by both the sequence and the topology of the DNA substrate, but many questions remain about how R-loops form and the 3-dimensional structures that they adopt. Here we represent an R-loop as a word in a formal grammar called the R-loop grammar and predict R-loop formation. We train the R-loop grammar on experimental data obtained by single-molecule R-loop footprinting and sequencing (SMRF-seq). Despite not containing explicit topological information, the R-loop grammar accurately predicts R-loop formation on plasmids with varying starting topologies and outperforms previous methods in R-loop prediction. Author summaryR-loops are prevalent triple helices that play regulatory roles in gene expression and are involved in various diseases. Our work improves the understanding of the relationship between the nucleotide sequence and DNA topology in R-loop formation. We use a mathematical approach from formal language theory to define an R-loop language and a set of rules to model R-loops as words in that language. We train the resulting R-loop grammar on experimental data of co-transcriptional R-loops formed on different DNA plasmids of varying topology. The model accurately predicts R-loop formation and outperforms prior methods. The R-loop grammar distills the effect of topology versus sequence, thus advancing our understanding of R-loop structure and formation.

genomics↗