UCSF

Advances in sequencing methodologies have produced a data glut; our ability to generate sequences exceeds our ability to understand the evolutionary selective pressures that shape them. The work presented in thesis consists of a two-pronged approach to study these pressures. The first approach employs an informatics-based analysis of high-throughput experimental data to detect selective pressures within protein substructures. The second uses the lac operon to quantify the relative impact of three selective pressures by measuring fitness effects of mutations under different induction conditions. Our results confirm our ability to detect selective pressures at the protein substructure level, as well as measure different fitness effects for different types of mutations. Furthermore, we uncovered surprising revelations about the lac operon itself, one of the most well-studied experimental systems in molecular biology. Finally, I present the methods used in this research as a general and robust approach for testing evolutionary models.