Molecular interaction networks have emerged as a powerful data source for answering a plethora of biological questions ranging from how cells make decisions to how species evolve. The availability of such data from multiple organisms allows for their analysis from an evolutionary perspective.
Gene duplication plays an important role in the evolution of genomes and interactomes, and elucidating the interplay between how genomes and interactomes evolve in light of gene duplication is of great interest. In order to achieve this goal, it is important to develop models and algorithms for analyzing network evolution, particularly with respect to gene duplication events.
The contributions of my thesis are four-fold. First, I developed a new genotype model that combines genomes with regulatory network, and a population genetic framework for simulating the evolution of this genotype. Using the simulator, I established explanations for gene duplicability. Second, I developed novel algorithms for probabilistic inference of ancestral networks from extant taxa, in a phylogenetic setup. Third, I conducted data analyses focusing on whole-genome duplication in yeast, and established a rate of protein-protein interaction networks, and devised a method for generating hypotheses about gene duplicate fates from network data. Fourth, and not least, I investigated the role of networks in defining adaptive models for gene duplication. In summary, my thesis contributes new analytical tools and data analyses that help elucidate and understand the interplay between gene duplication at the genomic and interactomic levels.