A simple model captures key characteristics of biological non-deterministic genotype-phenotype maps

By connecting genotypic mutations to the higher-order phenotypes relevant for selection, genotype-phenotype (GP) maps play a key role in evolution. GP maps are typically investigated using computational models of molecular phenotypes (for example, RNA secondary structures and simplified models of protein tertiary and quaternary structures), but GP map concepts are relevant beyond these specific models and so a systematic, model-independent approach is needed. This can be achieved by characterising GP maps in terms of properties like phenotypic frequencies, mutational robustness and evolvabilities, which can be computed for any given GP map. This approach has given insight into the shared features of GP maps and their evolutionary relevance. However, this progress is largely limited to the simplest case, where each genotype corresponds to a single, categorical phenotype. Here, I turn to a more realistic, but also more complex non-deterministic (ND) treatment, where each genotype generates an ensemble of phenotypes: I start by comparing the ND GP maps of the three biophysical models mentioned above using a recently proposed framework. Then, I find a simpler, synthetic map, which replicates key shared features for a range of modelling choices, suggesting that few ingredients are needed for these features to appear and highlighting the importance of non-linearities in GP maps. This synthetic ND GP map can be useful as a realistic, conceptually and computationally simpler model for future analyses of ND GP maps, their properties and their implications for evolutionary processes.