Evolution is one of the most fundamental processes in biology, but it can be notoriously difficult to observe directly. Evolutionary change accumulates over many thousands of generations as a result of a relentless competition between different genetic variants in a population. Except in a few rare cases, the intermediate steps of this competition are only partially preserved in the fossil record or the diversity of present-day species. This makes it hard to obtain the empirical data necessary for building and testing theories about the adaptive process. The present work aims to fill this void by constructing a high-resolution 'molecular fossil record' documenting 60,000 generations of evolution in experimental populations of bacteria. The unique duration of this experiment --- almost a quarter of the time since the human-chimpanzee split --- allows one to directly observe how the tempo of molecular evolution changes over long evolutionary timescales, and the biological replication will show which aspects of this process change when the evolutionary tape is replayed. These data will play a key role in testing existing theories of adaptation in a constant environment, and will help suggest new ways to model the evolution of large microbial populations.
To achieve these goals, this study will leverage new experimental techniques for massively multiplexed DNA sequencing to measure genetic diversity in the frozen ``fossil record'' of a well-studied evolution experiment in Escherichia coli. Combined with new bioinformatic techniques, these regularly preserved, whole-population samples allow one to trace the trajectories of new mutations as they arise and compete for dominance in the population. The ?molecular fossil records? that emerge from this analysis will constitute the longest and most detailed view of the dynamics of molecular evolution in any natural or experimental population. They will also generate a wealth of genetic data that will be used to compare patterns of molecular convergence and parallelism across populations and through time. These new measurements will be combined with a recently developed computational framework in order to test long-standing theoretical predictions about the dynamics of evolution in a constant environment.
