Effects of bias in GP maps: Cosmos — All that is, or was, or ever will be

Effects of bias in GP maps

guillefix 4th November 2016 at 2:43pm

Arrival of the frequent

The Arrival of the Frequent: How Bias in Genotype-Phenotype Maps Can Steer Populations to Local Optima See notes at Arrival of the frequent.

–The structure of the genotype–phenotype map strongly constrains the evolution of non-coding RNA. See notes

Probabilistic bias in genotype-phenotype maps. See more here: http://dingleresearch.weebly.com/publications.html

Self-assembling polyominoes model: A tractable genotype–phenotype map modelling the self-assembly of protein quaternary structure

More.... Modeling the evolution of molecular systems from a mechanistic perspective Adaptive dynamics under development-based genotype–phenotype maps Why self-incompatibility in the Brassicaceae is totally cool

Robustness and evolvability

3. The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype–phenotype maps. Features observed in several GP maps (including the simple Fibonacci GP map they use as a model):

Common features of GP maps

Genetic correlations greatly increase mutational robustness and can both reduce and enhance evolvability

random null model: that maintains the number of genotypes mapping to each phenotype, but assigns genotypes randomly

Genetic correlations

neutral correlations can be quantified by the robustness to mutations, which can be many orders of magnitude larger than that of the null model, and crucially, above the critical threshold for the formation of large neutral networks of mutationally connected genotypes which enhance the capacity for the exploration of phenotypic novelty. Thus neutral correlations increase evolvability.

non-neutral correlations: Compared to the null model:

i) If a particular (non-neutral) phenotype is found once in the 1-mutation neighbourhood of a genotype, then the chance of finding that phenotype multiple times in this neighbourhood is larger than expected;

ii) If two genotypes are connected by a single neutral mutation, then their respective non-neutral 1-mutation neighbourhoods are more likely to be similar;

iii) If a genotype maps to a folding or self-assembling phenotype, then its non-neutral neighbours are less likely to be a potentially deleterious non-folding or non-assembling phenotype.

Non-neutral correlations of type i) and ii) reduce the rate at which new phenotypes can be found by neutral exploration, and so may diminish evolvability, while non-neutral correlations of type iii) may instead facilitate evolutionary exploration and so increase evolvability.

Examples of GP map bias

model GP maps for protein tertiary (Emergence of Preferred Structures in a Simple Model of Protein Folding, Structural determinant of protein designability),
quaternary structure. Self-assembling polyominoes model (A tractable genotype–phenotype map modelling the self-assembly of protein quaternary structure),
Gene regulatory networks ( Highly designable phenotypes and mutational buffers emerge from a systematic mapping between network topology and dynamic output, Evolvability and robustness in a complex signalling circuit)
and development (An End to Endless Forms: Epistasis, Phenotype Distribution Bias, and Nonuniform Evolution)

suggesting that some of the results discussed in this paper for RNA may hold more widely in biology