In order to develop a predictive science of infectious disease, an understanding of the range and intensity of evolutionary forces that shape genomes is required. The genome of an organism, i.e. the coding and non-coding regions, as well as extranuclear DNA such as plasmids, mitochondrial and plastid genomes, are all subject to different evolutionary pressures. The evolutionary process can generally be regarded as a combination of three major forces: mutation, selection and genetic drift. The extent and force of these evolutionary pressures remain to be fully characterized. A thorough understanding of the means by which genomes change overtime, and hence provide a new repertoire of genes and gene products which generate novel phenotypes such as increased virulence, resistance to drugs or transition into a new geographical niche, is an absolute requirement for the generation of predictive measures of infectious disease. We propose to use a combination of disciplines including evolutionary biology, comparative genomics, bioinformatics, and population genetic theories and methodologies to identify and characterize the evolutionary forces that shape infectious disease genomes. We will use the malaria parasite species as a model system for our analysis. The genomes of eight species of Plasmodium parasite are being sequenced to varying degrees of completion, which represents the largest body of multi-species data available from one eukaryotic genus. We propose to (1) chronicle the evolution of Plasmodium chromosome organization;(2) determine the patterns of selective constraints across coding and non-coding sequences;(3) study the genetic variation and population structure of the most prevalent human malaria species. Our studies combine theoretical, quantitative and experimental approaches to achieve these aims. By providing a framework of analysis of several species of one major disease-causing pathogen, identification and characterization of the evolutionary forces that shape other genomes of infectious disease agents, and hence infectious disease phenotypes, should be possible.