Abstract
Organisms have genomes of vastly different sizes. A human genome has 3.2 billion base pairs, whereas the organism with the longest genome, the herb plant Paris japonica, has a genome with 150 billion base pairs (Ron Milo and Phillips 2015). How and why an organism evolves to have a genome of a specific size has been an open question in biology for decades. This thesis proposes a new toy model, heavily inspired by and building upon the logic gate model expressed in Kashtan and Alon’s 2005 paper, for which we can address the relation between genome size and evolution precisely. We use our model to analyze how genome size affects the number of mutations and the time required for new functions to be evolved. We find that, within our model, at small genome sizes both the time and the number of mutations to evolve decreases as genome size increases. At larger genome sizes, however, we see that time to evolve is independent of genome size and that the number of mutations to evolve a new function scales linearly with genome size. Finally, we study the space of evolved networks that have the desired function and uncover very interesting results demonstrating a lack of correlation between the size of the genome and the evolved computing network size. By understanding the dynamics at play in our model, we hope to gain intuition into how evolution and genome size influence each other in the real world.