Experimental evolution and real fitness landscapes

Evolution can be thought of as a type of hill climbing optimization problem. Mutations in a genetic sequence are the incremental changes in the system. Occasionally, the mutation will improve the output of the genetic sequence, and allow faster replication, which we call fitness. Natural selection will enrich these faster replicating individuals at the expense of slower replicating individuals and push populations of individuals toward "peaks" in the local search space. In the case of genetic sequences, this space is referred to as sequence space. However, the search space in biological systems is astronomically large. Consider a sequence 100 bp long, a fraction of a typical gene, has 4^100=1 x 10^60 possible combinations of the nucleotides A, G, C and T. Due to this complexity, many question remain about the nature of real fitness landscapes, and how they constrain and promote evolutionary optimization and innovations. My research attempts to approach these questions by studying the effects of mutations in vast collections of RNA molecules using high-throughput sequencing. I will present research questions and some of our approaches to analyze and visualize this data.