Data from: Maximum mutational robustness in genotypephenotype maps follows a selfsimilar blancmangelike curve
Data files
Jul 02, 2023 version files 1.62 MB

hp24_components.csv

hp5x5_components.csv

README.md

rna12_components.csv

rna12_theta.csv

rna12.csv

rna12.mat

rna12abstract.mat

rna15_components.csv

rna15.csv

rna15.mat

rna15abstract.mat
Abstract
Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayer’s graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sumsofdigits function from number theory. Interestingly, genotypephenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sumsofdigits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayer’s graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarsegrained and derive a formula and associated bounds for the transition probabilities between such phenotypes.
Methods
Usage notes
CSV files can be opened easily. MATLAB (or associated python packages) can open the .mat files. MATLAB or Octave can be used to load the .m scripts.