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Maximum mutational robustness in genotype–phenotype maps follows a self-similar blancmange-like curve

Article by Vaibhav Mohanty , Sam F. Greenbury , Tasmin Sarkany , Shyam Narayanan , Kamaludin Dingle , Sebastian E. Ahnert, Ard A. Louis
  • Published in 2023
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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 organized 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 sums-of-digits function from number theory. Interestingly, genotype–phenotype maps for RNA secondary structure and the hydrophobic-polar (HP) model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits 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 coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.

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key
Maximummutationalrobustnessingenotypephenotypemapsfollowsaselfsimilarblancmangelikecurve
type
article
date_added
2023-08-02
date_published
2023-01-22

BibTeX entry

@article{Maximummutationalrobustnessingenotypephenotypemapsfollowsaselfsimilarblancmangelikecurve,
	key = {Maximummutationalrobustnessingenotypephenotypemapsfollowsaselfsimilarblancmangelikecurve},
	type = {article},
	title = {Maximum mutational robustness in genotype–phenotype maps follows a self-similar blancmange-like curve},
	author = {Vaibhav Mohanty , Sam F. Greenbury , Tasmin Sarkany , Shyam Narayanan , Kamaludin Dingle , Sebastian E. Ahnert, Ard A. Louis},
	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 organized 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 sums-of-digits function from number theory. Interestingly, genotype–phenotype maps for RNA secondary structure and the hydrophobic-polar (HP) model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits 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 coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.},
	comment = {},
	date_added = {2023-08-02},
	date_published = {2023-01-22},
	urls = {https://royalsocietypublishing.org/doi/10.1098/rsif.2023.0169},
	collections = {food,unusual-arithmetic},
	url = {https://royalsocietypublishing.org/doi/10.1098/rsif.2023.0169},
	urldate = {2023-08-02},
	year = 2023
}