How many mutations does it take to evolve a new function?

In a Boolean-network genotype-phenotype map, the number of mutations needed to reach a new Boolean function scales with network size as a power law, with an exponent set by local map geometry.

• Why it matters: Answering how many mutations are required is difficult because genotype-to-phenotype maps are extremely high dimensional, making the mutation-to-function relationship hard to quantify.
• What they did: A Boolean-network model was used where genotype is encoded by network nodes and edges, phenotype by the Boolean function computed, and evolution occurs via edge rewiring with node count fixed; mutation counts were computed versus node number.
• The result: The mutation requirement follows a power-law in the number of nodes, and the exponent depends on local geometrical features of the genotype-phenotype map, highlighting how map structure shapes evolutionary time to new functions.

AI summary of the abstract · may contain errors · not medical advice · read the paper.