Structural properties of genotype–phenotype maps

Ahnert, Sebastian E.

doi:10.1098/rsif.2017.0275

Cited by 94 publications

(138 citation statements)

References 55 publications

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“…This leads us to conclude that both molecules are remarkably robust to mutation, and that proteins are likely to be slightly more robust than RNAs based upon the balance of all the evidence. This finding corroborates some previous theoretical studies that suggest proteins and RNAs have similar overall robustness to mutation (31,32) .…”

Section: Discussionsupporting

confidence: 92%

“…Previous comparisons of protein and RNA have involved computational analysis of neutral networks: a collection of related sequences that give rise to the same phenotype. Earlier analysis using reduced genetic codes (e.g., G+C for RNA and hydrophobic:hydrophilic for protein) (20,(29)(30)(31)(32) , found that RNA networks differed from protein networks, being larger (more robust) but also less compact. More recent work has shown that this is dependent on the mathematical framework used, and suggested that proteins and RNAs are more similar (31,32) .…”

Section: Stabilitymentioning

confidence: 99%

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The genetic robustness of RNA and protein from evolutionary, structural and functional perspectives

Sibaeva

McGimpsey

et al. 2018

Preprint

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The reactions of functional molecules like proteins and RNAs to mutation affect both host cell viability and biomolecular evolution. These molecules are considered robust if function is maintained despite mutations. Proteins and RNAs have different structural and functional characteristics that affect their robustness, and to date, comparisons between them have been theoretical. In this work, we test the relative mutational robustness of RNA and protein pairs using three approaches: evolutionary, structural, and functional. We compare the nucleotide diversities of functional RNAs with those of matched proteins. Across different levels of conservation, we found the nucleotide-level variations between the biomolecules largely overlapped, with proteins generally supporting more variation than matched RNAs. We then directly tested the robustness of the protein and RNA pairs with in vitro and in silico mutagenesis of their respective genes. The in silico experiments showed that proteins and RNAs reacted similarly to point mutations and insertions or deletions, yet proteins are slightly more robust on average than RNAs. In vitro , mutated fluorescent RNAs retained greater levels of function than the proteins. Overall this suggests that proteins and RNAs have remarkably similar degrees of robustness, with the average protein having moderately higher robustness than RNA as a group. Significance StatementThe ability of proteins and non-coding RNAs to maintain function despite mutations in their respective genes is known as mutational robustness. Robustness impacts how molecules maintain and change phenotypes, which has a bearing on the evolution and the origin of life as well as influencing modern biotechnology. Both protein and RNA have mechanisms that allow them to absorb DNA-level changes. Proteins have a redundant genetic code and non-coding RNAs can maintain structure and function through flexible base-pairing possibilities. The few theoretical treatments comparing protein and RNA robustness differ in their conclusions. In this experimental comparison of protein and RNA, we find that they have remarkably similar degrees of overall genetic robustness.

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Section: Discussionsupporting

confidence: 92%

Section: Stabilitymentioning

confidence: 99%

The genetic robustness of RNA and protein from evolutionary, structural and functional perspectives

Sibaeva

McGimpsey

et al. 2018

Preprint

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“…For this reason, they are referred to in the literature as neutral networks (NNs) [3,4]. A characteristic feature of all known GP maps is the strongly heterogeneous distribution of the abundance (number of nodes) of their NNs [5,6]. A few NNs are huge, typically percolating the whole genotype space, whereas most of them are small.…”

mentioning

confidence: 99%

Statistical theory of phenotype abundance distributions: A test through exact enumeration of genotype spaces

García-Martín¹,

Catalán²,

Manrubia³

et al. 2018

EPL

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The evolutionary dynamics of molecular populations are strongly dependent on the structure of genotype spaces. The map between genotype and phenotype determines how easily genotype spaces can be navigated and the accessibility of evolutionary innovations. In particular, the size of neutral networks corresponding to specific phenotypes and its statistical counterpart, the distribution of phenotype abundance, have been studied through multiple computationally tractable genotype-phenotype maps. In this work, we test a theory that predicts the abundance of a phenotype and the corresponding asymptotic distribution (given the compositional variability of its genotypes) through the exact enumeration of several GP maps. Our theory predicts with high accuracy phenotype abundance, and our results show that, in navigable genotype spaces -characterised by the presence of large neutral networks-, phenotype abundance converges to a log-normal distribution.

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“…This approach allowed us to explore a wide range of possible responses beyond the details of underlying molecular mechanisms. Compared to the commonly studied genotype-to-phenotype mapping, which describes how genetic variation affects phenotypes and emphasizes a mechanistic perspective [31][32][33], the environment-to-phenotype mapping provides a phenomenological perspective by describing organisms as a set of input-output relations that can be measured in experiments. This description is potentially useful for studying evolution, since the same form of phenotypic responses may be naturally selected even if it is implemented by different molecular mechanisms.…”

Section: B Relation To Experimentsmentioning

confidence: 99%

Environment-to-phenotype mapping and adaptation strategies in varying environments

Xue

Sartori

Leibler

2019

Proc. Natl. Acad. Sci. U.S.A.

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Biological organisms exhibit diverse strategies for adapting to varying environments. For example, a population of organisms may express the same phenotype in all environments ("unvarying strategy"), or follow environmental cues and express alternative phenotypes to match the environment ("tracking strategy"), or diversify into coexisting phenotypes to cope with environmental uncertainty ("bet-hedging strategy"). We introduce a general framework for studying how organisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the longterm growth rate of a population. The previously studied strategies emerge as special cases of our model: the tracking strategy is favorable when environmental cues are accurate, whereas when cues are noisy, organisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet-hedge. Our model of the environment-tophenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation, which can even be random.

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Structural properties of genotype–phenotype maps

Cited by 94 publications

References 55 publications

The genetic robustness of RNA and protein from evolutionary, structural and functional perspectives

The genetic robustness of RNA and protein from evolutionary, structural and functional perspectives

Statistical theory of phenotype abundance distributions: A test through exact enumeration of genotype spaces

Environment-to-phenotype mapping and adaptation strategies in varying environments

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