Symmetry and modularity emerge from the algorithmic nature of evolution.
Ard Louis ( University of Oxford )
Symmetry and modularity are ubiquitous in biology. But why do these properties arise? Here we argue that, rather than being favoured by natural selection, simpler and therefore more symmetric or modular phenotypes emerge because they are easier to discover in evolutionary search. We interpret genotype-phenotype maps as Turing machines, which may allow us to apply concepts from algorithmic information theory such as the coding theorem of Solomonoff and Levin, which suggests that phenotypes with low complexity are exponentially more likely to appear as potential variation than high complexity ones are. We find evidence for this scaling in protein quaternary structure, RNA secondary structure and gene-regulatory networks. This algorithmic picture suggests that low complexity phenotypes are not only easier to find but are also more likely to be symmetric, modular and mutationally robust than higher complexity ones are.