Applications of statistical physics to genome assembly and protein folding

This dissertation describes work on computational approaches to two problems of biological relevance, one practical and one theoretical. Both have a statistical ensemble at their heart. One is the practical problem of reconstructing a genome sequence from sequences sampled by the Whole Genome Shotgun method. A new method is described which has been successfully applied as part of ten genome sequencing projects and uses an iterative self-consistent approach to finding a solution. The method is robust enough to assemble polymorphic datasets. The second, theoretical, problem describes an investigation of the nature of the folding transition state of a simplified model for protein folding. Here, simplified dynamics of various model proteins are used to collect model protein conformations which have the defining properties of the transition state, and this ensemble of conformations is characterized. For each model, transition state conformations can be classified into one or two classes. The conformations in each class share a common partially-structured nucleus.