A Simulation Study of Protein Family Degree Distribution

The focus of this thesis is the degree distribution of protein family network graphs. We propose three models of evolution that generate a protein family. The first one uses Sequence Alignment to quantify protein relationship while the second uses the number of mutations accumulated on proteins. The last model incorporates explicitly preferential attachment. Following many other studies, we consider three operations: duplication, gene death and mutation. The main result that we report from the three models is that although exponential distribution is the best fit of the data, power law distribution fits the data well with the rates of evolution found in many studies.