Reply to Ocklenburg and Mundorf: The interplay of developmental bias and natural selection

Johnston, Iain G.; Dingle, Kamaludin; Greenbury, Sam F.; Camargo, Chico Q.; Doye, Jonathan P. K.; Ahnert, Sebastian E.; Louis, Ard A.

doi:10.1073/pnas.2205299119

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…Less information is required to describe bonding patterns that lead to higher symmetry, and thus such phenotypes have a higher probability of appearing upon random mutations [ 3 ]. One could imagine extending this preference for symmetry, modulated by processes such as symmetry breaking [ 71 ], to larger-scale developmental processes (see [ 72 , 73 ] for a discussion). In other cases, including the RNA secondary structures and branching morphologies (see ref [ 74 ]), different signatures of simplicity need to be employed to identify processes that can be described by shorter algorithms, which should be easier to find through random mutations.…”

Section: Discussionmentioning

confidence: 99%

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’s biomorphs

Martin,

Camargo,

Louis

2024

PLoS Comput Biol

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Biomorphs, Richard Dawkins’s iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations can fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems.

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Section: Discussionmentioning

confidence: 99%

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’s biomorphs

Martin,

Camargo,

Louis

2024

PLoS Comput Biol

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“…Since less information is needed to describe bonding patterns that lead to higher symmetry, such phenotypes are much more likely to appear upon random mutations [3]. One could easily imagine extending this preference for symmetry to larger-scale developmental processes (see [111,112] for a discussion).…”

Section: Discussionmentioning

confidence: 99%

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’ biomorphs

Martin

Camargo

Louis

2023

Preprint

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Biomorphs, Richard Dawkins' iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong Occam's-razor-like preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations will fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems, which would have implications for longstanding debates about internal versus external causes in evolution.

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“…Returning to the question of bias, in future work, it would be interesting to incorporate different structure prediction methods [ 81 ], especially RNA tertiary structure prediction, if and when it becomes available [ 82 ]. Furthermore, it would be interesting to study the interplay between bias and selection [ 66 , 83 ], whether natural or artificial (for example, investigating if the ‘arrival of the frequent’ can be observed experimentally). Possible ways this could be implemented include experimentally via estimating the fitness of RNA molecules [ 84 , 85 , 86 ], or via experimentation combined with deep learning methods to elucidate fitness landscapes, as has been done recently for RNA ligase ribozymes [ 87 ].…”

Section: Discussionmentioning

confidence: 99%

Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts

Ghaddar

Dingle

2023

Life

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An important question in evolutionary biology is whether (and in what ways) genotype–phenotype (GP) map biases can influence evolutionary trajectories. Untangling the relative roles of natural selection and biases (and other factors) in shaping phenotypes can be difficult. Because the RNA secondary structure (SS) can be analyzed in detail mathematically and computationally, is biologically relevant, and a wealth of bioinformatic data are available, it offers a good model system for studying the role of bias. For quite short RNA (length L≤126), it has recently been shown that natural and random RNA types are structurally very similar, suggesting that bias strongly constrains evolutionary dynamics. Here, we extend these results with emphasis on much larger RNA with lengths up to 3000 nucleotides. By examining both abstract shapes and structural motif frequencies (i.e., the number of helices, bonds, bulges, junctions, and loops), we find that large natural and random structures are also very similar, especially when contrasted to typical structures sampled from the spaces of all possible RNA structures. Our motif frequency study yields another result, where the frequencies of different motifs can be used in machine learning algorithms to classify random and natural RNA with high accuracy, especially for longer RNA (e.g., ROC AUC 0.86 for L = 1000). The most important motifs for classification are the number of bulges, loops, and bonds. This finding may be useful in using SS to detect candidates for functional RNA within ‘junk’ DNA regions.

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Reply to Ocklenburg and Mundorf: The interplay of developmental bias and natural selection

Cited by 5 publications

References 11 publications

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’s biomorphs

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’s biomorphs

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins’ biomorphs

Random and Natural Non-Coding RNA Have Similar Structural Motif Patterns but Differ in Bulge, Loop, and Bond Counts

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